ERG 2081 Final Report: Drug Mechanism of Action (MoA) Prediction Model

Introduction of Drug Mechanism of Action (MoA):

     In the past, scientists derived drugs from natural products or were inspired by traditional remedies. Drugs were put into clinical use decades before the biological mechanisms driving their pharmacological activities were understood. With the advent of more powerful technologies, today, drug discovery has changed from the serendipitous approaches of the past to a more targeted model based on an understanding of the underlying biological mechanism of a disease.

     In this new framework, scientists seek to identify the mechanism of action of new drugs, which usually includes mention of the specific molecular targets to which the drug binds, such as an enzyme or receptor. By knowing the interaction between a certain site of a drug and a receptor, other drugs can be formulated in a way that replicates this interaction, thus producing the same therapeutic effects. This method is used to create new drugs.

Motivation: How do we better predict drug-target interactions (DTIs) to determine the MoAs(Mechanism of action) of a new drug?

     One approach is to treat a sample of human cells with the drug and then analyze the cellular responses with algorithms that search for similarity to known patterns in large genomic databases, such as libraries of gene expression or cell viability patterns of drugs with known MoAs (e.g. Connectivity Map and DepMap).

     In this project, I build a model from the train set to automatically labels each case in the test set as one or more MoA classes, which is a multi-label classification problem. I evaluate the model based on the average value of the logarithmic loss function applied to each drug-MoA annotation pair in the test set. Details of datasets will be provided below.

Data Discription:

     There are several databases that are available in providing a unique dataset that combines gene expression and cell viability data. CMAP and LINCS are the two that were most used in previous research.

     However, both CMAP and LINCS required each cell type to be profiled separatel and previous efforts have largely focused on studying responses in a small number of cell line contes. But perturbation responses areoften context-specific, reflecting their interaction with the cell’s underlying genomic or functional features. For example, targeted drugs may elicit responses only in cell lines harboring particular oncogenic mutations or expressing certain genes, making observed results specific to the particular cell line models chosen. 1.&hl=zh-CN&as_sdt=0&as_vis=1&oi=scholart)

     In 2020, a new approach to present gene expression signatures across multiple cell lines in parallel is developed as MIX-Seq. MIX-Seq combines:

1. the ability to pool hundreds of cancer cell lines and co-treat them with one or more perturbations;

2. the power of scRNA-seq to simultaneously profile the cells’ responses and resolve the identity of each cell based on single-nucleotide polymorphism (SNP) profiles.

     The profile of each perturbation (specified by drug, time, dose and control type) is created by measuring simultaneously (within the same samples) human cells’ responses to drugs in a pool of more than 100 different cell types, which tackled profiling the responses of many diverse cell lines to a given perturbation and thus assessed context-specific effects.

Following the instruction provided by the MOA prediction task on one kaggle competition, I access DepMap and get the PRISM Repurposing 19Q3 Primary Screen dataset and try to understand how datasets in this competition comes. The primary PRISM Repurposing dataset contains the results of pooled-cell line chemical-perturbation viability screens for 4,518 existing drugs screened against 578 cancer cell lines.

import pandas as pd
import plotly.express as px
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import torch
import random
from sklearn.model_selection import train_test_split
import os
from sklearn.preprocessing import QuantileTransformer
from sklearn import preprocessing
from sklearn.decomposition import PCA
import pickle
from math import *
import seaborn as sns

drug_original = pd.read_csv('primary-screen-replicate-collapsed-logfold-change.csv')
drug_original.head()

	Unnamed: 0	BRD-A00077618-236-07-6::2.5::HTS	BRD-A00100033-001-08-9::2.5::HTS	BRD-A00147595-001-01-5::2.5::HTS	BRD-A00218260-001-03-4::2.5::HTS	BRD-A00376169-001-01-6::2.5::HTS	BRD-A00520476-001-07-4::2.5::HTS	BRD-A00546892-001-02-6::2.5::HTS	BRD-A00578795-001-04-3::2.5::HTS	BRD-A00758722-001-04-9::2.5::HTS	...	BRD-K98557884-001-01-6::2.5::MTS004	BRD-K99077012-001-01-9::2.332734192::MTS004	BRD-K99199077-001-16-1::2.603211317::MTS004	BRD-K99431849-001-01-7::2.500018158::MTS004	BRD-K99447003-335-04-1::2.37737659::MTS004	BRD-K99506538-001-03-8::2.5::MTS004	BRD-K99616396-001-05-1::2.499991421::MTS004	BRD-K99879819-001-02-1::2.5187366::MTS004	BRD-K99919177-001-01-3::2.5::MTS004	BRD-M63173034-001-03-6::2.64076472::MTS004
0	ACH-000001	-0.015577	-0.449332	0.489379	0.206675	0.272730	0.021036	-0.025460	0.467158	-0.736306	...	0.429238	0.204841	0.150055	-0.575404	-0.101247	0.399233	-0.127658	-0.141651	-1.153652	0.510464
1	ACH-000007	-0.095730	0.257943	0.772349	-0.438502	-0.732832	0.779201	0.426523	-1.288508	-0.476133	...	-0.471486	0.212998	-0.123230	0.625527	0.383198	0.212031	0.349225	-0.387439	-0.831461	0.323558
2	ACH-000008	0.379480	-0.596132	0.548056	0.422269	-0.216986	0.081866	0.145335	-0.570841	-0.512119	...	-0.111951	0.534787	0.206642	-0.410153	-0.560722	-0.036088	0.158071	0.171043	-3.947090	0.099310
3	ACH-000010_FAILED_STR	0.118890	-0.231615	0.621937	-0.202707	-1.005139	-0.213739	0.020246	-0.795278	NaN	...	0.200605	-0.075356	0.610310	-0.019413	-0.202971	0.218158	-0.411009	-0.181540	-3.010225	0.090652
4	ACH-000011	0.145346	-0.499274	0.267470	0.157804	-0.272286	0.207768	0.004464	-0.191680	-0.310375	...	-0.076863	0.026002	0.139921	-0.261704	0.085339	0.447482	0.164620	-0.565251	-4.110627	0.222394

5 rows × 4687 columns

For each perturbation (specified by a sig_id, used to querey the known MOA notation from Dr. Insight package in R, I randomly select 100 out of 578 cancer cell lines c0-c99 and took out the most correlated differentially expressed genes (setting a p-value threshold at 0.05) g0-g771. This is a unique dataset that combines gene expression and cell viability data. Then I split thwm into training features set and testing features set.

pd.options.display.max_columns = None
train_features = pd.read_csv('train_features.csv')

train_features.head()

	sig_id	cp_type	cp_time	cp_dose	g-0	g-1	g-2	g-3	g-4	g-5	g-6	g-7	g-8	g-9	g-10	g-11	g-12	g-13	g-14	g-15	g-16	g-17	g-18	g-19	g-20	g-21	g-22	g-23	g-24	g-25	g-26	g-27	g-28	g-29	g-30	g-31	g-32	g-33	g-34	g-35	g-36	g-37	g-38	g-39	g-40	g-41	g-42	g-43	g-44	g-45	g-46	g-47	g-48	g-49	g-50	g-51	g-52	g-53	g-54	g-55	g-56	g-57	g-58	g-59	g-60	g-61	g-62	g-63	g-64	g-65	g-66	g-67	g-68	g-69	g-70	g-71	g-72	g-73	g-74	g-75	g-76	g-77	g-78	g-79	g-80	g-81	g-82	g-83	g-84	g-85	g-86	g-87	g-88	g-89	g-90	g-91	g-92	g-93	g-94	g-95	g-96	g-97	g-98	g-99	g-100	g-101	g-102	g-103	g-104	g-105	g-106	g-107	g-108	g-109	g-110	g-111	g-112	g-113	g-114	g-115	g-116	g-117	g-118	g-119	g-120	g-121	g-122	g-123	g-124	g-125	g-126	g-127	g-128	g-129	g-130	g-131	g-132	g-133	g-134	g-135	g-136	g-137	g-138	g-139	g-140	g-141	g-142	g-143	g-144	g-145	g-146	g-147	g-148	g-149	g-150	g-151	g-152	g-153	g-154	g-155	g-156	g-157	g-158	g-159	g-160	g-161	g-162	g-163	g-164	g-165	g-166	g-167	g-168	g-169	g-170	g-171	g-172	g-173	g-174	g-175	g-176	g-177	g-178	g-179	g-180	g-181	g-182	g-183	g-184	g-185	g-186	g-187	g-188	g-189	g-190	g-191	g-192	g-193	g-194	g-195	g-196	g-197	g-198	g-199	g-200	g-201	g-202	g-203	g-204	g-205	g-206	g-207	g-208	g-209	g-210	g-211	g-212	g-213	g-214	g-215	g-216	g-217	g-218	g-219	g-220	g-221	g-222	g-223	g-224	g-225	g-226	g-227	g-228	g-229	g-230	g-231	g-232	g-233	g-234	g-235	g-236	g-237	g-238	g-239	g-240	g-241	g-242	g-243	g-244	g-245	g-246	g-247	g-248	g-249	g-250	g-251	g-252	g-253	g-254	g-255	g-256	g-257	g-258	g-259	g-260	g-261	g-262	g-263	g-264	g-265	g-266	g-267	g-268	g-269	g-270	g-271	g-272	g-273	g-274	g-275	g-276	g-277	g-278	g-279	g-280	g-281	g-282	g-283	g-284	g-285	g-286	g-287	g-288	g-289	g-290	g-291	g-292	g-293	g-294	g-295	g-296	g-297	g-298	g-299	g-300	g-301	g-302	g-303	g-304	g-305	g-306	g-307	g-308	g-309	g-310	g-311	g-312	g-313	g-314	g-315	g-316	g-317	g-318	g-319	g-320	g-321	g-322	g-323	g-324	g-325	g-326	g-327	g-328	g-329	g-330	g-331	g-332	g-333	g-334	g-335	g-336	g-337	g-338	g-339	g-340	g-341	g-342	g-343	g-344	g-345	g-346	g-347	g-348	g-349	g-350	g-351	g-352	g-353	g-354	g-355	g-356	g-357	g-358	g-359	g-360	g-361	g-362	g-363	g-364	g-365	g-366	g-367	g-368	g-369	g-370	g-371	g-372	g-373	g-374	g-375	g-376	g-377	g-378	g-379	g-380	g-381	g-382	g-383	g-384	g-385	g-386	g-387	g-388	g-389	g-390	g-391	g-392	g-393	g-394	g-395	g-396	g-397	g-398	g-399	g-400	g-401	g-402	g-403	g-404	g-405	g-406	g-407	g-408	g-409	g-410	g-411	g-412	g-413	g-414	g-415	g-416	g-417	g-418	g-419	g-420	g-421	g-422	g-423	g-424	g-425	g-426	g-427	g-428	g-429	g-430	g-431	g-432	g-433	g-434	g-435	g-436	g-437	g-438	g-439	g-440	g-441	g-442	g-443	g-444	g-445	g-446	g-447	g-448	g-449	g-450	g-451	g-452	g-453	g-454	g-455	g-456	g-457	g-458	g-459	g-460	g-461	g-462	g-463	g-464	g-465	g-466	g-467	g-468	g-469	g-470	g-471	g-472	g-473	g-474	g-475	g-476	g-477	g-478	g-479	g-480	g-481	g-482	g-483	g-484	g-485	g-486	g-487	g-488	g-489	g-490	g-491	g-492	g-493	g-494	g-495	g-496	g-497	g-498	g-499	g-500	g-501	g-502	g-503	g-504	g-505	g-506	g-507	g-508	g-509	g-510	g-511	g-512	g-513	g-514	g-515	g-516	g-517	g-518	g-519	g-520	g-521	g-522	g-523	g-524	g-525	g-526	g-527	g-528	g-529	g-530	g-531	g-532	g-533	g-534	g-535	g-536	g-537	g-538	g-539	g-540	g-541	g-542	g-543	g-544	g-545	g-546	g-547	g-548	g-549	g-550	g-551	g-552	g-553	g-554	g-555	g-556	g-557	g-558	g-559	g-560	g-561	g-562	g-563	g-564	g-565	g-566	g-567	g-568	g-569	g-570	g-571	g-572	g-573	g-574	g-575	g-576	g-577	g-578	g-579	g-580	g-581	g-582	g-583	g-584	g-585	g-586	g-587	g-588	g-589	g-590	g-591	g-592	g-593	g-594	g-595	g-596	g-597	g-598	g-599	g-600	g-601	g-602	g-603	g-604	g-605	g-606	g-607	g-608	g-609	g-610	g-611	g-612	g-613	g-614	g-615	g-616	g-617	g-618	g-619	g-620	g-621	g-622	g-623	g-624	g-625	g-626	g-627	g-628	g-629	g-630	g-631	g-632	g-633	g-634	g-635	g-636	g-637	g-638	g-639	g-640	g-641	g-642	g-643	g-644	g-645	g-646	g-647	g-648	g-649	g-650	g-651	g-652	g-653	g-654	g-655	g-656	g-657	g-658	g-659	g-660	g-661	g-662	g-663	g-664	g-665	g-666	g-667	g-668	g-669	g-670	g-671	g-672	g-673	g-674	g-675	g-676	g-677	g-678	g-679	g-680	g-681	g-682	g-683	g-684	g-685	g-686	g-687	g-688	g-689	g-690	g-691	g-692	g-693	g-694	g-695	g-696	g-697	g-698	g-699	g-700	g-701	g-702	g-703	g-704	g-705	g-706	g-707	g-708	g-709	g-710	g-711	g-712	g-713	g-714	g-715	g-716	g-717	g-718	g-719	g-720	g-721	g-722	g-723	g-724	g-725	g-726	g-727	g-728	g-729	g-730	g-731	g-732	g-733	g-734	g-735	g-736	g-737	g-738	g-739	g-740	g-741	g-742	g-743	g-744	g-745	g-746	g-747	g-748	g-749	g-750	g-751	g-752	g-753	g-754	g-755	g-756	g-757	g-758	g-759	g-760	g-761	g-762	g-763	g-764	g-765	g-766	g-767	g-768	g-769	g-770	g-771	c-0	c-1	c-2	c-3	c-4	c-5	c-6	c-7	c-8	c-9	c-10	c-11	c-12	c-13	c-14	c-15	c-16	c-17	c-18	c-19	c-20	c-21	c-22	c-23	c-24	c-25	c-26	c-27	c-28	c-29	c-30	c-31	c-32	c-33	c-34	c-35	c-36	c-37	c-38	c-39	c-40	c-41	c-42	c-43	c-44	c-45	c-46	c-47	c-48	c-49	c-50	c-51	c-52	c-53	c-54	c-55	c-56	c-57	c-58	c-59	c-60	c-61	c-62	c-63	c-64	c-65	c-66	c-67	c-68	c-69	c-70	c-71	c-72	c-73	c-74	c-75	c-76	c-77	c-78	c-79	c-80	c-81	c-82	c-83	c-84	c-85	c-86	c-87	c-88	c-89	c-90	c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0	id_000644bb2	trt_cp	24	D1	1.0620	0.5577	-0.2479	-0.6208	-0.1944	-1.0120	-1.0220	-0.0326	0.5548	-0.0921	1.1830	0.1530	0.5574	-0.4015	0.1789	-0.6528	-0.7969	0.6342	0.1778	-0.3694	-0.5688	-1.1360	-1.1880	0.6940	0.4393	0.2664	0.1907	0.1628	-0.2853	0.5819	0.2934	-0.5584	-0.0916	-0.3010	-0.1537	0.2198	0.2965	-0.5055	-0.5119	-0.2162	-0.0347	-0.2566	-1.1980	0.3116	-1.0330	0.2208	-0.1143	-0.0251	0.0350	0.4233	-0.1221	-0.4927	0.6119	0.4715	-0.0351	-0.1605	-0.6750	0.2263	0.5790	0.9588	0.6061	-0.1300	0.6611	-0.3136	-0.2020	0.0251	0.1670	-0.4195	0.6551	-0.7395	-0.7047	-0.5369	0.0314	-0.5239	-0.3406	-0.6332	-1.0330	-0.3763	-0.8550	0.7348	-0.2226	-0.1959	-0.0005	-2.1440	1.7320	0.0190	-0.1188	0.4050	-1.5840	-0.3011	0.2048	-1.1230	-1.5590	-1.7980	0.7017	-1.0390	0.0958	0.4527	0.5455	-0.0828	-0.5364	0.5522	-0.7768	-0.8044	-0.1181	-1.2560	0.1593	-0.6998	-1.7470	0.7120	-0.7635	-0.0290	1.2670	0.4734	-0.9902	0.4391	-0.1188	-0.0348	0.4676	1.9750	0.0545	1.2880	1.2260	0.0486	-0.8622	-1.2770	-0.1810	-0.2600	-0.5130	-0.0227	-0.2257	-0.7349	0.3116	-0.8774	0.3539	0.2833	-0.3501	-0.4372	-0.4646	-0.4940	0.5297	1.2420	-1.1870	0.4382	1.1360	-0.1758	0.3858	-1.2530	-0.1779	0.5655	-0.1696	0.1464	-0.1973	-0.4959	0.2655	0.4279	-0.0202	-0.1091	-0.7799	-0.8062	-0.3239	0.2411	-1.0170	-0.5592	-0.4386	0.0590	0.3816	0.0817	-0.8457	0.5989	-0.4194	0.0858	0.7449	1.6820	-0.2471	-1.4130	-0.2031	-0.2256	-0.6217	0.4969	0.1109	-0.3284	0.2469	-0.2786	0.2318	-0.5274	0.5698	-0.2100	1.4050	-0.1078	0.1064	-0.2164	0.0917	0.4796	-0.1509	0.7374	-0.7406	-0.5210	-0.0491	0.8366	2.0720	0.4171	0.2383	1.0990	-0.2268	-0.3183	0.5875	-0.2213	-0.8059	1.4560	-0.3744	-0.5810	0.7226	0.1093	0.7356	-1.2170	0.6242	0.7148	0.3128	0.2482	-1.0320	-0.5883	-1.9170	-0.2457	0.5389	-0.2066	-0.6200	0.9635	1.1080	-0.0549	0.8528	-1.6510	0.1414	-0.2369	0.2736	0.1218	-0.3546	-0.1706	0.2143	0.2421	0.0236	0.5628	0.4498	-1.2160	0.1905	0.3205	-0.7976	-0.4980	-0.3072	0.7621	0.3802	0.8749	-5.6910	-0.5780	-1.3310	0.7077	0.7656	-1.0850	-4.2300	0.1147	-0.2898	-0.6454	1.0590	1.0820	-0.0111	-0.0030	0.2083	0.2706	0.1489	0.7500	-0.6775	1.1370	-0.6083	0.6781	-0.1734	0.7350	-0.0750	0.8785	-0.0273	-0.6809	-0.6896	0.3126	-0.0746	-0.1634	-0.1318	0.1369	0.4765	-0.0140	-0.0411	0.0819	-0.4803	0.3860	-0.4232	0.0018	0.1365	-0.4100	-0.0684	0.6743	-0.3253	-0.7053	-0.7026	0.4308	-0.4998	-0.3225	-0.5908	0.0631	0.9894	-0.2418	-0.0950	-0.0402	-0.5224	0.3137	0.1922	-0.6259	-0.6170	0.3810	-0.1991	-0.3726	0.1163	0.4642	-1.8840	-0.4574	0.6154	-0.5105	-0.3627	0.3073	0.3873	-0.0835	0.2406	0.8298	0.1425	-0.0162	0.5513	-1.1080	-0.2111	-0.0272	0.9574	0.1556	0.8994	0.8328	-0.6052	-0.2273	0.5473	-0.1076	-0.1274	-0.3065	0.7828	-1.1430	-0.2158	-0.3083	0.5523	0.2218	0.0652	0.4911	-0.0847	-0.4622	0.7352	-0.3754	-0.0539	-0.5360	-0.2167	0.2862	-0.7244	0.4862	0.3098	0.3203	-1.1360	-0.0003	0.0323	-0.3259	0.2418	0.2289	0.1368	-0.2966	-0.3741	0.5207	0.1586	0.2070	-0.2473	1.0300	-0.3336	-0.0813	1.4350	-0.2065	0.3192	1.7600	-1.3010	-1.8500	-0.0301	0.5843	2.9260	0.0880	-0.1301	0.4300	-0.8048	0.1346	0.2102	0.8176	0.1524	-1.3750	1.0530	1.1810	-0.5394	0.3665	-0.5807	0.4673	0.0675	-0.3645	-0.8796	-0.6843	-0.5152	-0.8100	1.0640	-0.1973	-0.4108	-0.4925	-1.0510	-0.5257	-0.1831	-0.1411	-0.7326	0.0000	0.7155	1.5050	-0.6908	1.2700	0.5581	-0.4096	-0.0324	-0.0513	-0.3070	0.7375	-0.3648	0.1698	0.7127	-0.3804	0.3381	-0.4014	-0.0093	-0.1907	0.7591	-0.2120	1.0600	0.0995	-0.4298	0.9586	0.5436	0.8127	-0.1461	0.3318	0.9664	-0.8808	-0.2304	0.2416	0.0396	-0.9080	0.9310	0.6799	-1.3560	0.2437	-1.4880	0.9209	0.4848	0.6587	-0.3046	0.3768	0.8593	-0.9440	0.5416	-0.7895	-0.3494	0.4762	2.3150	-0.2225	-0.1816	-0.3624	0.2554	0.0205	0.3210	0.4219	-0.2075	0.0236	-0.4579	0.5735	-0.5794	-0.0312	0.4597	0.1564	0.4239	-0.4168	1.1010	0.7867	-0.7898	-0.0942	0.2404	0.0250	0.1980	-0.7132	-0.4471	0.2010	0.6466	0.6063	-0.7012	0.5861	0.4844	-0.1026	-0.3578	-0.5311	0.5993	1.2340	-0.0841	-0.7955	0.4247	0.1726	1.4460	0.7659	-1.1540	-0.0514	0.2890	-0.8723	0.1143	1.0390	-0.4714	0.5738	-0.8244	2.5560	-1.1780	0.4859	-0.5094	-0.2978	0.4729	-0.3151	-0.5117	0.3726	0.1583	0.6409	0.1861	-0.4818	-1.5260	0.5918	-0.1791	0.0702	0.9369	-0.7034	0.0407	-0.5628	0.5371	-0.4948	-0.2678	0.9640	-0.7682	-0.1171	1.0650	0.0403	-0.2536	-1.9680	0.7657	0.3726	-2.3880	-0.5841	-0.0110	-0.0977	0.1230	-0.0452	-4.0410	-0.1743	0.1225	0.6353	-0.1063	0.1242	-0.8044	-2.5040	2.0270	-0.0181	-0.1780	0.6145	-0.8319	-0.5172	-0.1023	0.7075	-0.2458	0.6410	0.7450	-0.8205	-0.1733	-0.2182	0.8320	1.0150	-0.2779	-0.3271	-1.2450	0.6923	2.2630	0.5050	-0.6080	0.4942	-0.5155	0.1552	0.5134	0.7408	-0.1477	-0.2136	-0.6129	0.4357	1.2690	-0.3858	0.8604	0.0958	3.3940	-0.0452	0.6513	0.0941	1.6230	1.2990	0.6629	-0.2984	-0.3423	-1.2190	-1.3040	-0.5645	0.7661	-0.0819	0.6308	0.7729	-0.0072	0.2032	-0.3999	0.4572	-0.8976	0.2599	0.8406	0.6590	0.5739	-1.0410	0.2930	-0.2461	-0.1803	-0.3313	-0.8521	0.0299	-1.6130	0.0490	0.5186	-0.7712	0.1361	-0.5311	0.8418	-0.0203	0.0685	-0.2004	-0.3432	-0.8311	-0.2598	0.8483	0.6825	0.4016	0.2968	-0.6905	0.1019	-0.9237	-0.1007	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1	id_000779bfc	trt_cp	72	D1	0.0743	0.4087	0.2991	0.0604	1.0190	0.5207	0.2341	0.3372	-0.4047	0.8507	-1.1520	-0.4201	-0.0958	0.4590	0.0803	0.2250	0.5293	0.2839	-0.3494	0.2883	0.9449	-0.1646	-0.2657	-0.3372	0.3135	-0.4316	0.4773	0.2075	-0.4216	-0.1161	-0.0499	-0.2627	0.9959	-0.2483	0.2655	-0.2102	0.1656	0.5300	-0.2568	-0.0455	0.1194	-0.3958	-1.1730	0.4509	1.9250	1.1010	0.2648	-0.3615	0.1710	-0.8900	1.2730	-0.5036	-0.2277	-0.7339	-0.4283	-0.1231	-0.6328	0.4759	0.3249	-0.7408	-0.5814	1.7840	-0.1087	1.0350	0.4087	-0.6948	-0.8026	0.0954	-0.4054	0.1197	0.1956	0.0720	-0.2463	-0.1084	0.4008	-0.5857	0.8933	0.2842	1.1870	0.7311	0.7734	-1.5920	-1.3500	0.0055	-0.6507	-0.3979	0.6976	0.1383	0.2558	0.2563	0.0563	-0.3854	-0.6528	-0.1740	-0.0188	-0.4910	-1.3820	0.4300	-0.3965	0.1346	0.7129	-0.0724	-0.8599	-0.3863	-0.0948	-0.5932	-0.1630	-0.2356	0.2319	0.0487	0.7302	0.0271	-0.1482	0.2702	0.8842	-0.2944	-0.4810	0.0268	-1.2140	1.7630	0.7860	0.0232	0.0152	0.0804	-0.8073	1.1340	-0.2540	-0.1643	-0.6529	1.1480	0.2172	-0.0493	0.0806	-0.8065	-0.3062	-0.4078	0.5039	0.7468	-0.0415	0.9550	-0.4778	-1.3910	0.1903	-0.4695	-0.5351	-0.3524	-0.9459	0.0287	0.5038	-0.4026	0.2609	0.1743	0.5582	0.5020	-0.6926	-0.0407	1.0120	0.1305	0.1049	-0.1423	0.0882	1.1880	-0.5056	-0.0163	-1.4020	0.6522	1.2160	-0.0478	0.3713	0.2215	0.2610	0.3841	-0.1476	-1.2850	-0.6565	0.2697	0.0919	0.2341	1.3360	-0.1854	0.0325	0.1416	-0.2420	-0.2166	-0.5338	0.1229	-0.2280	-0.3149	0.7871	-0.1145	-0.2067	-0.3810	0.3166	-1.4640	-0.1521	0.4040	0.6048	0.1126	-0.2919	-0.3401	1.8500	0.0767	0.4833	-0.6428	0.8914	-0.6113	-1.1600	-0.5689	-0.3027	-0.1273	-0.8117	1.0240	0.3938	0.1875	0.2138	1.4080	0.5441	0.8406	-0.9672	-0.4611	0.1144	1.2280	0.1734	0.8120	0.9384	0.4147	-0.0678	0.3877	-0.0554	0.5726	-0.2251	-0.3640	0.2817	-1.4580	0.4062	-0.5457	0.0207	0.3992	0.0000	-1.6820	-0.5257	0.0354	-0.9122	0.5680	-0.1172	-0.3771	0.2739	0.5264	1.0880	-0.2243	0.1435	0.2113	0.1145	0.3929	0.4099	-1.3350	-0.0047	0.3889	-2.8660	-0.4587	0.5828	0.3794	-0.2398	-0.6023	0.3300	0.7609	0.7782	0.6288	0.3997	0.3334	-0.4084	0.0322	0.8100	0.0790	0.2957	0.0552	0.2857	-0.2803	0.2148	-0.0999	-0.8585	0.0424	0.2288	0.2725	-0.8289	-0.4467	0.5038	1.1150	-0.4685	-0.2131	2.7230	-0.4210	0.6654	-0.3241	0.4205	-0.0983	-0.7719	-0.6461	0.4525	0.3128	-0.0982	-0.4904	-0.5194	-0.0573	0.8627	0.2022	-0.3246	0.0000	-0.1836	1.1160	0.0896	0.5995	0.0872	-0.4782	1.3920	0.7363	1.0350	-0.0988	-0.1391	-0.0334	0.1495	-0.0284	-0.5422	-1.4280	-2.0420	0.7898	-0.3544	0.6622	-0.3950	-0.0612	0.3440	-0.0350	0.3131	0.1551	0.0415	0.2605	0.4880	-0.3697	0.1219	-0.0901	0.7550	-0.0588	-0.4411	-0.1349	1.6910	0.4883	-0.9540	-0.1068	-0.3089	-0.2037	-1.1410	0.6719	0.3316	-0.3645	-0.1505	-1.5620	0.1618	1.4340	2.3300	-0.5334	0.2491	-0.1300	0.0820	0.3372	0.1284	-0.2834	0.2392	-0.5780	-0.5125	0.5464	-0.0003	1.4300	0.0483	0.6572	-0.5502	-0.6803	0.5432	1.0340	0.9739	0.2095	-0.8197	5.5660	0.0976	0.7329	-0.4685	-0.1022	-0.6943	1.4360	0.0524	-1.2900	0.3202	-0.2365	0.0452	0.1015	-0.1654	0.2342	0.7119	0.9402	0.8471	0.8067	0.9035	-0.3166	-0.2157	-0.3248	0.7639	-0.4159	-0.7689	0.6705	0.1015	-0.2108	0.8483	0.1878	-0.0445	-0.2075	0.2641	-0.8241	3.4430	-0.3536	-0.9675	-0.6392	0.1663	0.3741	-0.0849	-1.5230	-0.6122	0.2376	-0.7045	0.9229	-0.1109	0.1455	1.0250	-0.3828	0.1192	0.0670	0.6260	0.0354	0.1407	0.4225	0.6169	0.1912	-0.7532	0.1076	-1.5640	-0.0900	-0.0638	0.4470	0.2506	-1.1270	0.1764	-0.6652	0.5373	-0.4762	2.1760	-0.2850	1.0640	-0.8141	0.8071	0.7380	-0.6021	0.4574	0.2496	-1.1630	0.2737	-1.4720	-0.5201	0.3198	-0.5713	0.9142	-0.6790	0.7054	-5.2370	0.9811	0.1245	-0.1504	-0.6305	-0.8063	-0.9589	0.4972	-1.1140	-0.0072	0.2877	-0.2530	-0.9674	0.4881	-0.2506	0.5595	0.0116	0.4315	-0.7103	0.0661	-0.3377	-1.0560	0.8337	0.2122	0.2911	1.2920	0.6318	0.7765	0.7975	-0.3077	-0.3372	-1.6120	0.4096	0.7901	0.0100	-0.4658	-0.5450	-0.5073	-0.1380	-0.4848	0.9155	0.9955	-0.9648	-0.8853	0.0999	-1.0340	0.5836	0.1950	-0.6917	-0.4196	-1.2280	-0.3105	0.1353	-0.5391	0.2404	-0.5610	-0.1294	-0.0806	0.7456	-0.6391	0.0853	-0.6206	0.6729	-0.8522	-0.2415	0.0761	0.3558	0.9662	0.1707	-0.9712	0.0832	0.5572	0.6003	-0.0062	0.0322	-0.1043	0.2711	-0.2471	0.3055	-0.2862	0.4155	-0.4424	-0.5932	0.0273	0.1344	-1.3420	-0.2685	0.2151	-0.5127	0.6447	0.8874	-0.4330	-0.5393	0.1479	0.4881	-0.1831	1.3670	-0.3372	0.3190	0.3824	0.7297	0.1558	-0.5803	-0.6928	1.2090	0.5312	1.1430	0.9513	-0.6138	-0.7623	0.0863	1.2540	-1.2850	-0.0172	-1.1420	0.5320	-0.2722	0.0000	-0.1406	-0.9510	0.9837	-0.2797	-1.1940	-0.4952	-0.6391	0.4099	-0.2789	-1.7590	-0.4139	-0.8009	-0.9295	0.8543	1.2990	-1.1020	-0.6565	-1.0050	-0.8245	-0.9344	1.1400	0.8501	-0.7063	0.3696	-1.0050	-0.4687	-1.2080	1.4330	-0.3249	-0.3488	0.8468	-0.0415	-0.6929	-3.2430	-1.9270	0.7375	-0.5561	0.2121	0.1149	-0.1857	-0.0585	-1.1910	-0.0211	2.0930	-0.1148	0.5500	-0.2247	-0.2341	0.1500	1.0330	0.4735	-0.3925	1.0250	-0.4841	-0.2616	-0.2097	1.6210	-0.0198	-0.3301	-0.7962	-0.3305	0.8353	-0.0824	0.2448	-0.6899	0.0095	-0.2209	-0.4061	-0.2806	1.5450	-0.0336	0.8536	-0.3959	-0.0771	1.1700	-0.3825	1.2050	-0.2530	-0.1584	0.3260	0.0056	-0.8074	-0.3990	0.5275	0.1397	-0.8016	0.0213	0.7234	0.3140	0.1219	0.2062	0.0692	0.4442	-0.5133	0.4945	-0.0779	-0.6745	-0.8910	-2.4460	0.6951	0.1032	0.1847	-0.9964	0.2310	-0.4867	0.0663	-4.1870	0.6669	0.8576	-0.4833	-0.0817	2.2200	-0.8233	-1.0760	-0.2645	-0.7521	0.8955	0.4854	0.1402	0.1514	0.0730	0.4431	0.5035	0.9635	0.6474	0.4754	-0.3977	-0.0720	-0.7758	0.2326	-0.4880	0.0326	-0.3358	0.0632	-0.0542	-0.2321	-0.4092	0.2212	0.7369	0.8979	-0.5004	-0.3708	0.1372	-1.2270	3.1050	0.0204	-0.0961	0.2919	-0.4584	0.0556	0.9844	-0.6653	-0.3092	-0.1507	-0.9481	-0.1966	0.8554	0.5839	-1.6840	0.0648	0.5845	0.0891	-0.5025	-0.1047	0.3764	-1.4800	-0.2947	0.2745	-0.5234	0.1575	-0.1214	0.0476	0.1657	1.0310	-0.5338	0.0224	-0.4831	0.2128	-0.6999	-0.1214	-0.1626	-0.3340	-0.3289	-0.2718	0.0927	0.2723	0.2772	0.7776	0.3679	0.5696	0.2835	1.4080	0.3745	0.6775	0.7787	0.2074	1.1110	0.2433	0.2010	0.7094	-0.4210	-0.2274	0.4045	0.1138	0.9950	0.0087	0.4864	-0.0140	0.0164	0.1759	0.6654	0.3556	0.8175	0.0474	0.5589	0.4481	0.1888	0.3712	0.7837	0.6860	0.4123	0.1560	-0.1203	-1.2780	0.6743	0.7358	0.1334	-0.5193	0.2281	0.0158	-0.5658	0.6065	0.6259	0.4716	0.0453	0.5036	1.0750	0.2111	0.4759	0.1408	0.8763	0.4611	0.8784	-0.4291	0.4083	0.0319	0.3905	0.7099	0.2912	0.4151	-0.2840	-0.3104	-0.6373	0.2887	-0.0765	0.2539	0.4443	0.5932	0.2031	0.7639	0.5499	-0.3322	-0.0977	0.4329	-0.2782	0.7827	0.5934	0.3402	0.1499	0.4420	0.9366	0.8193	-0.4236	0.3192	-0.4265	0.7543	0.4708	0.0230	0.2957	0.4899	0.1522	0.1241	0.6077	0.7371
2	id_000a6266a	trt_cp	48	D1	0.6280	0.5817	1.5540	-0.0764	-0.0323	1.2390	0.1715	0.2155	0.0065	1.2300	-0.4797	-0.5631	-0.0366	-1.8300	0.6057	-0.3278	0.6042	-0.3075	-0.1147	-0.0570	-0.0799	-0.8181	-1.5320	0.2307	0.4901	0.4780	-1.3970	4.6240	-0.0437	1.2870	-1.8530	0.6069	0.4290	0.1783	0.0018	-1.1800	0.1256	-0.1219	5.4470	1.0310	0.3477	-0.5561	0.0357	-0.3636	-0.4653	-0.8346	0.1113	-0.7615	-0.4983	0.4552	-0.0978	-0.2661	0.6058	0.2686	-0.6191	-0.4444	1.2540	0.0621	-0.1757	-1.4370	0.5553	0.8346	-0.3334	-0.2933	-0.1644	-1.1280	-0.0292	0.0939	0.5916	0.6809	0.7821	0.6801	0.9634	0.4057	-0.0708	-1.3570	0.2306	-0.4130	-0.1406	0.3078	-0.4273	-0.8073	1.0050	-0.0678	-0.0617	-0.1562	0.4080	0.7163	-0.2040	-0.9246	0.8540	0.4286	0.3396	1.6870	-0.6283	0.2548	0.6822	0.0955	-0.3061	0.7335	-0.7646	-0.3154	0.6849	0.7327	0.0000	-0.4462	-0.9601	-0.2600	-0.7443	0.1388	-0.1204	-0.0079	-0.8991	-0.1627	-0.3827	-0.1245	-0.7449	-0.4446	-0.2475	-0.0184	0.2551	-0.9627	1.0660	0.1807	0.2413	0.9176	-0.0486	-0.3885	-0.2843	0.3051	-0.1750	-0.3465	-0.6194	-0.6888	0.6535	-0.0302	-0.0747	-0.0095	-0.4059	-0.7744	-1.1260	-0.8119	0.3927	1.8060	-0.0104	2.4450	-0.1512	0.6194	1.7050	-1.8500	-0.8219	-0.2035	-0.6910	0.5166	0.1713	-0.2872	-1.4870	0.3598	0.3011	0.4400	1.7920	-4.1810	2.1840	-0.9974	0.2307	-0.8124	0.8455	-2.7010	-0.9168	-1.1480	0.0825	-0.6908	-0.4245	-0.3494	-0.1320	1.1870	-2.6240	-1.6640	0.1667	-1.2800	0.7258	-0.3007	0.5677	0.4284	-0.2485	-2.1940	-0.0611	0.0544	3.6780	-0.9055	2.0350	1.1970	-0.3382	0.4036	1.1240	-0.0200	0.6208	-1.0140	0.4891	-0.9596	0.6812	0.8989	1.1700	-0.0548	0.0158	0.3535	0.4584	-0.2650	-0.7341	-0.2659	-0.2204	-0.2573	-0.0987	0.0000	-0.9180	-0.4061	-0.1563	-0.5587	0.7254	-0.1942	0.7268	0.4797	0.2154	-0.4764	0.1271	1.0900	0.9388	-0.5565	0.0423	-0.0731	-0.9985	-0.3970	0.2657	-0.8142	0.4578	-1.0520	1.0170	0.2057	0.0000	-0.6368	0.0580	0.0256	0.3221	1.0280	0.3711	0.8571	-0.0999	1.1750	1.0790	-0.2471	0.4541	-1.1310	1.3710	-1.0650	-1.7880	-0.2968	-1.0720	-0.1389	0.6456	0.0163	0.7382	0.8752	0.1376	0.9276	-0.2786	0.1356	3.8460	0.4007	-0.3805	-0.3386	-1.2020	-2.6600	-0.4291	0.4423	-0.0138	-1.4390	-1.2520	-3.0580	-1.3900	0.5075	1.0470	-0.6240	0.3637	1.1530	-2.4060	-0.2155	0.1403	0.1304	-0.6842	0.7247	-0.3732	-0.3652	0.2583	0.2603	0.7571	0.5776	-0.1146	0.5044	-0.2182	-1.1640	0.1973	-0.1634	0.4679	-0.6327	0.0077	0.4912	-0.0439	0.0000	-0.7869	-0.3595	-0.7576	-0.5784	-1.1580	0.3820	-0.7059	0.3318	-0.2815	0.3059	-0.7775	0.2092	-0.2256	0.7167	0.1008	0.3419	-0.6242	-0.6133	-0.7581	-0.6950	0.4777	-0.3594	0.6317	0.7301	-0.5409	0.0328	1.0430	-1.5410	-0.5556	-0.6799	-0.0899	-0.0953	-0.1192	0.1494	-0.1420	-0.0760	4.9950	-0.7192	-0.5795	-1.3020	-0.0552	1.0100	0.2776	0.6188	-0.5809	-0.3133	0.4052	0.0645	-0.5867	-0.4081	-1.0730	0.8852	-0.1123	-0.7055	1.0370	-0.1787	-0.8260	0.2129	-0.1174	0.3113	-1.1920	0.8636	0.0000	0.7387	-0.5885	0.7911	0.3885	-3.8760	2.1570	0.0424	-0.2833	0.0316	0.6744	-0.8045	-0.8462	-0.4991	0.3607	-1.1120	-0.8808	1.2810	1.6090	-0.9422	0.6258	1.9910	-0.2280	-0.0571	0.4884	-0.2524	-2.6080	0.2484	-1.6240	-1.8640	-0.0814	-1.5910	-0.4724	-0.6393	0.5290	-0.3234	-0.6989	0.2333	-0.3057	-0.2485	-0.0931	0.3332	-1.5860	-0.6640	1.9000	0.9154	0.4955	0.6638	0.2452	-0.0018	0.4202	-3.0600	-0.9568	-0.6617	0.3672	1.6110	-1.6320	-0.5669	-0.3505	-0.7257	-0.4403	-0.8346	0.3099	-0.7949	0.2979	-1.5600	-0.6829	0.8864	-0.1460	-0.8346	-0.1508	-0.6829	0.5365	0.5518	0.8081	-0.5251	-0.0437	-0.2788	0.7479	-0.0791	0.0813	1.1550	0.1092	-0.0844	-0.0771	0.8328	1.2730	0.4051	-0.0829	-0.2115	0.9004	0.4302	0.0398	-0.0176	0.4419	0.7477	1.0620	-0.0858	-0.5853	0.7763	-4.3980	0.4945	0.0106	-0.4947	-1.4010	0.5584	-0.0285	0.5072	0.2086	-4.7380	0.8392	0.7598	0.5072	-1.3920	2.1010	-1.0860	1.0340	-0.5167	-1.9990	0.3732	-0.9049	-0.7799	0.9992	-0.5590	-0.0135	-4.8890	-0.1696	1.0460	0.0245	0.0970	0.1320	0.7928	0.6660	-0.1795	-0.4340	0.3210	0.0586	0.5718	-1.2530	-0.5652	-0.5416	0.1668	-0.0813	-0.1271	1.8260	0.0549	-0.7779	-0.1319	-0.9788	-0.0324	-0.8480	-0.2811	-0.8472	0.8832	-0.3841	-0.2430	0.0829	-0.0176	-1.7680	-0.3257	0.9746	0.4862	-0.7556	-0.5038	0.3430	0.1265	-1.0220	0.4076	-1.0590	0.2557	0.5379	0.2591	-0.9427	0.8284	0.0294	-0.4264	-0.4164	0.0000	0.0134	-0.9426	-0.2307	0.1802	-1.2700	-5.1640	-0.1248	0.4868	-0.7385	-0.2664	0.4148	0.4067	-0.2651	-0.4749	-0.7756	-1.2890	0.6985	0.0284	0.2087	-0.8303	-0.2733	-0.5637	-0.8866	-2.5160	-0.6209	-0.1428	-0.7385	-0.1108	0.5086	0.0811	0.4886	0.6869	0.5040	-0.4021	1.2190	0.7519	1.7730	-0.7739	1.0250	-0.2140	0.0086	-0.4949	0.1489	-1.8890	-0.1239	-0.0641	0.9610	-0.0255	0.5612	1.2830	0.5330	-0.5566	-0.7392	-0.3010	2.8890	1.3400	1.4570	4.4760	-1.5700	0.6040	2.2500	0.2751	-0.2307	0.8842	1.0470	0.8195	0.3975	-0.6864	-0.4417	1.6090	0.0000	0.1167	0.3657	-0.5192	-0.6763	-1.0520	1.2730	-0.3368	-0.9114	-0.7419	0.6620	-0.3058	0.7158	1.6390	-0.1177	0.5713	0.8512	0.7849	-0.3873	-0.4874	0.8361	0.8780	-0.2979	-0.6863	0.2327	0.9903	-0.3399	-0.5250	0.0083	-0.6169	1.3990	0.1680	-0.2512	0.4597	0.5315	0.7145	0.3622	0.0651	-0.3092	1.7750	0.5993	0.4922	-0.8939	-0.1007	-0.1343	1.1520	0.1996	-0.4331	0.1165	-1.0320	-1.2070	0.4614	0.0810	-0.2468	-1.0270	2.1400	-0.7917	-1.2830	-0.3885	0.8156	-0.9331	-0.7458	-0.5182	-0.2459	1.7540	-0.4985	0.1818	-1.9020	-3.6980	0.1066	1.3720	0.3859	0.8492	1.6340	0.3321	1.1220	0.5421	-1.1240	-0.3101	-0.6470	0.4192	-0.6892	0.1765	-0.1348	-0.1582	0.4049	0.2967	0.6300	0.7751	0.0000	0.7150	-0.3821	-0.0208	0.9317	-0.1150	0.2530	-0.5545	-0.7061	-0.4234	0.2350	-0.1255	0.3519	0.7924	0.8877	1.0540	0.5625	-0.4162	-0.1915	-0.2098	1.0540	-0.2885	-1.2540	0.2628	0.4860	-0.4954	-0.1840	-1.1110	-0.5649	-0.2617	2.9120	2.7720	0.1523	-1.1230	-0.5340	-0.7937	0.3457	-0.6136	-0.0706	-0.7474	0.1795	-0.4674	-0.1552	0.6338	0.5130	-0.3625	0.0873	0.2689	-0.2174	0.7069	-0.6456	0.6822	0.2237	-0.3601	1.2780	2.5770	0.2356	1.3230	-1.3730	-0.2682	0.8427	0.5797	0.3143	0.8133	0.7923	-0.1312	-1.4640	0.3394	-1.7790	0.2188	0.5826	-0.7513	0.0543	0.7182	-0.4159	0.3084	-0.0899	-0.0397	-0.5077	-0.5072	0.0100	-0.6915	0.0643	-0.8927	-0.2048	0.0773	0.5971	-0.1602	-0.2028	-0.2216	-1.1380	0.3668	-1.0620	-0.7736	0.6225	0.6274	-0.3038	0.1171	-0.0550	-0.0289	1.1740	-0.7937	-1.0610	-0.6409	0.3884	-1.0330	-0.0055	0.1212	-0.7020	-0.7065	-0.5192	-0.3181	0.1787	0.3354	0.1349	-0.3569	-0.8229	-0.2778	0.7047	-0.9726	-0.0553	-0.2143	0.0187	0.4497	0.2585	-0.5477	-0.7576	-0.0444	0.1894	-0.0014	-2.3640	-0.4682	0.1210	-0.5177	-0.0604	0.1682	-0.4436	0.4963	0.1363	0.3335	0.9760	-0.0427	-0.1235	0.0959	0.0690	-0.9416	-0.7548	-0.1109	-0.6272	0.3019	0.1172	0.1093	-0.3113	0.3019	-0.0873	-0.7250	-0.6297	0.6103	0.0223	-1.3240	-0.3174	-0.6417	-0.2187	-1.4080	0.6931
3	id_0015fd391	trt_cp	48	D1	-0.5138	-0.2491	-0.2656	0.5288	4.0620	-0.8095	-1.9590	0.1792	-0.1321	-1.0600	-0.8269	-0.3584	-0.8511	-0.5844	-2.5690	0.8183	-0.0532	-0.8554	0.1160	-2.3520	2.1200	-1.1580	-0.7191	-0.8004	-1.4670	-0.0107	-0.8995	0.2406	-0.2479	-1.0890	-0.7575	0.0881	-2.7370	0.8745	0.5787	-1.6740	-1.6720	-1.2690	3.0900	-0.3814	-0.7229	-0.0010	0.1353	-1.6400	-0.7483	-1.0820	-0.1521	0.0747	-0.4851	0.6636	-2.8230	-0.6035	-1.3550	2.0200	-0.5301	-1.3240	-0.5936	0.4331	0.0899	-1.5240	0.3936	1.4630	-1.2100	-1.2340	-1.0300	0.7977	0.2314	0.1186	1.5370	0.3868	-0.4038	0.9767	-0.9161	1.2720	1.3670	-3.3210	-1.0510	-0.7074	1.0380	-0.7296	-0.9796	0.1337	0.3262	0.5635	0.8961	1.0280	0.0508	-0.4742	1.1640	0.2764	1.6310	4.9160	2.3690	0.6832	-1.4810	1.4310	0.1686	-1.7220	0.0892	-0.8117	1.0390	-0.0851	2.0570	-0.7968	0.0000	1.6720	-0.1619	-0.9467	-0.4938	-0.2653	-0.7025	-0.3582	-0.1362	-2.0530	0.2815	-0.0158	-0.5371	-1.8970	0.0636	0.2328	-0.7206	-1.3960	0.6489	-0.2583	1.4160	-1.5420	0.4545	0.2297	0.8581	0.2128	0.0973	3.0850	0.0196	-0.3923	-1.2910	0.2836	-0.5197	-0.0278	1.0140	1.1300	-0.3831	-0.2767	0.5993	-0.5146	0.4468	1.7270	1.1450	0.8178	4.2300	-1.7170	-0.3004	-0.2569	-3.0280	-0.2357	-0.2941	-1.0680	-1.6470	0.5287	4.2110	-0.5516	0.8118	-0.8374	1.0510	1.1430	0.9099	-0.2411	-1.3680	1.0670	0.2782	1.4610	-0.5527	-1.0070	-0.0910	-1.0610	0.7465	3.4580	-0.5228	0.3133	-3.1620	-1.2130	0.0650	1.6530	-0.0022	0.0864	0.8989	-0.8746	-1.4210	-0.2400	-0.1266	-2.5660	3.5620	2.5130	-0.1470	0.3183	-0.0179	-0.7292	-0.4748	-0.2785	-1.3360	-0.8703	0.3079	1.0300	0.6138	-0.0781	0.6745	1.4640	0.1835	1.5390	0.7044	-0.4105	2.2950	-0.5759	-1.1730	0.0000	-0.7379	0.1739	-0.8528	-0.7338	-0.7139	0.2921	0.4294	0.7911	0.5141	-1.2560	1.3320	1.0130	-0.2829	-0.9621	-0.6012	-0.0755	-0.2506	0.4848	-1.0020	-0.3075	0.7056	-1.1260	-0.8238	0.6757	-2.9920	-0.8815	-0.1069	-1.8660	-1.6800	0.8285	-0.2857	0.7501	-0.2312	0.3080	-0.2261	-0.9338	0.7248	-0.5338	1.4540	-1.0730	0.9034	0.6597	-0.1388	-1.8380	0.1154	-0.2590	-0.0906	0.6501	0.6779	0.4718	-0.5355	-0.6934	1.0450	0.5419	-1.0160	0.4607	0.3340	-0.6615	-0.9769	-0.5336	0.9085	-0.4634	1.2270	-1.2540	0.1619	1.9800	0.6543	2.4890	-0.8378	0.6786	0.8357	-1.0510	-2.1760	-1.5850	-0.1538	0.0175	1.3440	0.7247	1.1290	0.1487	-1.1370	0.5676	0.5035	-0.8286	-0.4800	0.9560	1.5650	-1.1430	2.6370	-0.0323	0.2528	0.9815	0.4952	0.0000	0.4769	-1.8140	0.7224	0.9167	-0.8064	0.7627	-1.3140	0.3369	-1.0040	0.4941	-0.6865	-1.2470	0.5177	0.8631	-0.2207	1.6890	0.0278	0.8366	0.4719	-0.9539	0.3398	-2.0160	0.0752	-0.1248	-3.2040	0.9331	0.2451	1.2840	-0.1404	1.0270	-1.7620	-1.6500	-0.3145	-0.8277	0.3183	-0.7785	0.8227	-0.6364	1.3710	0.0733	0.3490	1.3710	-0.6559	-1.1310	0.1346	-1.4920	-0.2054	0.2347	-0.0750	-1.9230	0.3839	0.6633	-3.7210	0.0505	0.4819	1.3030	-0.0382	0.5461	-1.3620	1.7230	0.3278	-0.3219	0.0000	-0.3682	0.0165	0.7910	1.0430	-4.2100	0.0839	0.8239	-0.0379	0.7182	0.4608	0.0553	-1.0920	-0.3756	-0.8635	1.3170	0.5511	0.3855	-0.2890	2.3880	-1.2370	0.2282	5.3910	0.1787	1.6310	-0.2385	0.3620	-1.5570	-0.9610	-0.0340	-0.0033	-2.6650	0.2314	-0.1152	0.5762	-0.8791	0.6613	0.2965	2.1760	-0.4277	-0.4053	0.8295	-0.7285	-0.6666	0.3509	1.5660	0.0664	1.8810	0.9420	-0.9474	0.9560	-0.1804	-0.0288	0.0676	1.3760	0.0486	1.3590	1.2260	-2.9690	-1.8280	0.0039	1.2260	-0.4750	-0.3331	-1.0780	-0.3784	-0.0766	1.6780	0.2981	-2.0680	-0.3044	-0.2097	1.0480	0.3950	1.3800	0.0449	0.4697	-0.3887	-0.5060	0.7361	-0.1803	0.9406	1.9660	0.3990	-1.1830	-0.1672	0.2030	0.2905	0.0012	-1.4960	0.9815	0.8393	-0.9190	-0.7865	-0.3507	-1.3930	-1.0930	1.3840	1.6700	1.1610	2.2210	0.3359	1.3350	-0.1171	-2.0020	-1.0660	0.5548	-0.5673	1.8530	-1.4260	1.5990	-0.0636	-0.5135	-0.5080	1.7670	0.7666	-0.3262	1.0610	-0.8242	0.0786	1.4980	1.0900	-0.1133	0.0736	0.1619	0.6091	0.5473	-0.3697	-0.4561	-0.0279	0.6199	-1.6680	-0.2848	-1.8370	-1.2500	0.0657	0.2881	1.9540	-0.8217	-0.6751	-0.9215	-1.4850	1.6700	0.4746	-0.1840	1.6050	-0.7572	2.2510	0.2775	1.0940	-0.6423	3.1370	-0.8023	-1.0670	0.4539	-0.5028	-2.4080	3.9550	0.4539	-0.1322	-0.9284	0.4188	0.4942	2.9250	-1.4530	-0.6428	-0.0132	0.3487	0.5719	0.0626	-0.8656	0.2303	-1.3900	0.3094	-0.2177	-0.7207	2.3000	-0.5900	0.0718	2.2320	-0.2394	2.6350	0.1964	0.7290	0.8339	0.6475	0.7826	0.0964	-2.3770	0.5035	-0.9751	-1.4720	-1.0940	-0.3539	-0.0511	-2.0870	-1.4650	-1.1280	-1.8010	-1.9290	0.2277	-0.7521	0.1606	-1.1110	0.1934	-2.5680	-0.6381	-2.1540	0.0803	-0.0723	-0.0759	0.3699	0.4403	0.4006	-0.3634	0.7690	-0.7345	0.4005	1.2810	2.1750	0.6065	1.8980	0.8480	-0.5012	0.5735	-0.5937	0.0654	0.0622	-1.6000	-1.1830	0.0767	-1.8150	-0.8354	1.7400	-0.4051	0.4205	1.8750	0.8082	-1.5450	-1.3680	0.3007	-0.2897	0.9897	-1.0990	1.1170	-2.3370	-0.6219	1.0080	1.8850	1.0020	0.9497	-0.3868	-1.2350	-0.1629	-1.3350	0.4012	-1.1110	-0.9854	2.8250	0.0419	-0.2128	-0.8987	-1.3500	0.5189	1.5050	0.7685	-0.6352	-0.0799	-0.9627	0.0909	2.3190	-0.1951	-1.2550	0.6816	-0.9182	1.4530	0.4617	-0.1585	0.1581	0.2589	-1.3220	-1.3490	1.6700	1.6140	-0.0307	0.1174	-4.2470	0.1192	-0.2344	-0.2993	-0.7514	-2.3450	1.3640	1.0830	-0.2562	-0.5434	0.1641	-0.6745	-1.4150	-1.3860	-1.0970	1.3760	-3.5480	-0.7292	-2.3340	0.3811	-0.2867	-0.5258	-1.7020	-0.5638	-3.3060	-0.3645	-0.5911	1.4210	0.1478	1.6550	0.2889	1.1170	-0.0378	2.3980	1.2510	0.7589	1.4650	0.1003	-0.5949	0.4992	-0.7242	-0.5878	0.9478	-0.7887	-0.8408	-0.2256	0.3293	-0.0092	-1.1560	-1.2300	-1.3140	0.0000	2.2010	-0.1639	-0.7305	0.9258	-1.0620	0.6889	-0.9377	-0.7624	0.0610	-1.3920	0.2704	-0.3988	1.0400	0.5012	0.5485	1.5170	0.7751	1.0230	1.8570	-0.4721	0.5465	1.9580	0.3873	0.9512	0.2239	0.4428	-0.2822	-0.8175	1.4930	-0.0711	-1.6250	-0.4317	-0.0239	0.5046	-0.8061	0.9736	1.2540	2.0730	0.6604	-0.4758	-0.1766	-0.8959	0.6560	-0.0875	0.1082	-0.7158	0.0712	-0.1939	0.4286	-1.0220	0.9017	1.1750	-0.5760	0.7801	-0.1292	3.4310	1.2720	-0.4733	-2.0560	0.5699	0.1996	0.4374	0.1588	-0.0343	-0.3998	-4.5520	-2.7350	-1.9630	-2.8610	-1.2670	-2.5830	-0.5036	-3.1590	-1.8510	-2.2730	-4.6920	-1.2750	-1.4970	-0.0156	-1.9350	-1.3430	-0.5252	-2.7210	-0.2524	0.9197	-3.1220	-1.9180	-0.6761	-2.8360	-1.6890	-4.4990	-1.5190	-2.2730	-0.3250	0.1895	-2.4220	0.0670	-1.9870	-0.6482	-0.0023	-1.9930	-0.8042	-3.6160	-0.3487	-1.2560	-2.7220	-2.1970	-2.9030	-1.1030	-2.3020	-2.0030	-1.3390	-3.0160	-3.0040	-2.8880	-1.4910	-1.7960	-1.3670	-2.7830	-3.3290	0.1071	-2.5430	0.3747	-1.7540	-2.1220	-0.3752	-2.3820	-3.7350	-2.9740	-1.4930	-1.6600	-3.1660	0.2816	-0.2990	-1.1870	-0.5044	-1.7750	-1.6120	-0.9215	-1.0810	-3.0520	-3.4470	-2.7740	-1.8460	-0.5568	-3.3960	-2.9510	-1.1550	-3.2620	-1.5390	-2.4600	-0.9417	-1.5550	0.2431	-2.0990	-0.6441	-5.6300	-1.3780	-0.8632	-1.2880	-1.6210	-0.8784	-0.3876	-0.8154
4	id_001626bd3	trt_cp	72	D2	-0.3254	-0.4009	0.9700	0.6919	1.4180	-0.8244	-0.2800	-0.1498	-0.8789	0.8630	-0.2219	-0.5121	-0.9577	1.1750	0.2042	0.1970	0.1244	-1.7090	-0.3543	-0.5160	-0.3330	-0.2685	0.7649	0.2057	1.3720	0.6835	0.8056	-0.3754	-1.2090	0.2965	-0.0712	0.6389	0.6674	-0.0783	1.1740	-0.7110	-1.4470	1.0620	0.7888	-0.0848	0.1302	0.1224	0.9584	0.2126	0.6162	1.1570	1.1140	-0.9416	-0.3780	-0.8147	-0.0803	-0.3818	0.6877	0.0000	0.1776	1.1320	-0.1180	1.2130	0.7709	0.6745	0.7871	1.6410	0.1806	1.3480	1.1070	0.5392	0.0000	0.1255	-0.1556	-0.5436	0.8585	0.5801	0.9348	-0.7508	-1.1160	-0.7868	0.1675	-0.3088	0.6630	-1.7000	0.4253	-1.1700	-0.8736	0.6169	-1.2160	-0.1491	-2.2520	0.6823	1.4800	0.9068	-0.7982	0.8025	1.2990	1.0690	-0.1518	-0.8001	1.1940	-0.2334	0.2345	-0.4384	0.0968	-1.1880	-1.3030	0.6187	0.0000	1.1790	-0.8521	-0.2801	0.4686	-0.5360	-0.2246	-2.3510	-0.9180	0.3001	0.8598	-2.3350	-0.6522	0.0481	-1.6780	1.1890	-3.8480	0.7353	-1.0690	-1.3950	-0.6294	1.1970	-1.3880	1.2030	0.6597	0.6237	1.5330	2.0390	0.4533	-1.4210	-0.1871	-1.7040	0.9155	0.3770	0.9127	0.5022	0.0851	-2.4280	-0.8599	-0.3822	-1.7530	-0.4152	-0.5948	-0.2815	0.4478	0.0795	1.8640	-1.3230	0.7051	-1.9440	-0.5804	-2.4960	0.4042	0.0000	0.9283	1.4940	-0.8757	-0.3667	0.4272	-0.9715	0.3501	0.3622	-0.8955	0.1902	1.1520	0.6745	0.6135	-1.4480	-0.6243	-1.6000	-0.0913	0.5282	1.6050	0.5905	-0.8969	-0.9344	-0.9038	-0.5732	-0.6177	0.1723	-0.6239	-0.8446	-0.8357	0.6745	0.5846	-0.0990	0.8706	0.4460	-0.4033	-0.5549	0.2064	0.0216	1.5880	-1.0360	3.5090	-2.2770	0.8582	-0.3519	0.1611	-0.6091	0.6349	2.5350	0.8124	0.4877	0.5398	-1.6940	0.9118	-0.0297	-0.8432	-0.6745	-0.1088	-0.2959	0.0000	-3.3670	0.6184	-0.5558	-1.8510	0.8816	-0.4465	-0.1110	0.9283	0.4013	0.4805	-0.4941	0.5843	-0.2956	-0.5784	0.2703	-1.2210	0.2981	0.5394	-0.2124	-1.7750	0.6448	-0.4229	1.1780	-0.1187	1.3120	-0.3824	0.2817	-0.4712	-0.2025	0.7764	-0.9704	-0.3918	-1.8520	0.2075	-1.9210	-0.4632	0.3245	1.0200	-0.8012	-0.3692	1.5180	1.4300	-0.6103	0.0470	1.1480	0.1320	-0.3212	-0.0998	0.7510	0.1720	0.0564	0.4967	0.4296	-0.6745	-0.5648	0.3632	0.4002	-1.0400	0.5747	2.1570	-0.1172	-0.5832	0.7898	-0.3740	0.7584	-0.2999	-0.6644	0.2780	-0.5275	0.2495	0.1336	-0.6745	-0.4059	1.7520	0.0000	-0.3425	-1.7450	-1.5510	0.8668	-0.4081	-0.6331	1.0620	1.4220	0.7401	-1.2510	-0.6152	-0.3062	0.8265	-0.4067	1.3190	0.0000	-1.9420	0.4959	-0.6125	0.5715	-1.4620	0.1636	0.3380	-0.7069	1.2150	0.4886	1.7670	-1.1000	-0.1970	0.7510	0.2686	-0.0307	-0.4054	0.4745	-1.4690	1.3610	0.2954	-0.6984	-0.2638	-0.4027	0.0000	0.0000	-0.6595	0.0814	-1.1190	-0.1619	-0.1737	-1.1710	0.0000	-0.4046	1.5690	0.2072	1.0200	-0.3954	0.4725	1.2250	-0.1595	-1.2180	-1.7060	-0.5808	0.2080	0.5858	-0.9161	-0.4027	-1.6030	0.0000	0.0726	-1.0880	0.7548	-3.0190	-0.1245	0.4229	0.8929	0.3510	0.9119	0.4195	-0.8983	0.1720	-0.7305	0.4470	-0.4431	1.2480	0.5380	0.5362	-0.3009	0.6166	0.0873	-0.0925	0.0793	-0.1917	-0.9976	-1.3930	0.0000	-1.1660	-1.6810	-1.5350	-1.0990	0.3597	0.1084	-0.0231	-0.5027	-1.1990	0.7772	1.0630	1.1580	-1.0530	0.7561	4.1550	-1.7970	1.1130	-1.7720	-0.4640	0.7851	-0.0424	0.9072	0.1443	1.4130	-0.9213	0.3470	0.9218	0.3226	-0.0584	0.8650	1.6120	-0.5096	0.0333	-0.1938	1.6760	0.5895	0.5527	0.2188	0.0837	0.9530	0.5553	-0.6962	1.5520	1.6940	-1.4790	-1.0360	-0.7548	0.3278	-0.2446	1.2470	0.0000	1.1840	-0.5506	-0.5521	0.1645	0.1631	-0.2358	-0.4004	-0.6745	0.6745	-0.3815	0.9565	-0.2797	0.0558	-0.7133	-1.1760	-3.6890	1.4900	0.0856	0.1767	1.6960	-0.3000	0.0614	-0.6768	-0.5181	-0.6745	-1.6460	-1.1110	0.0000	-0.7498	-1.0240	-0.9353	0.7450	-1.4720	0.0630	-0.2657	0.9111	-0.5700	0.0000	-0.8002	0.6071	-0.1591	-0.1187	1.0380	-0.6050	1.2600	-0.4651	0.3986	-0.5505	0.5760	0.1933	-0.7694	0.5110	-1.3650	0.6212	-0.9800	0.6034	-0.5548	-1.0310	-0.3904	-0.4773	0.6852	0.1374	-0.7720	-0.5254	0.0000	0.4195	0.3163	-0.1094	-0.9861	-0.6909	0.2282	0.9321	1.8970	0.3187	-0.4870	1.5050	0.2405	0.5617	0.6745	0.0833	-0.0466	-0.8141	-0.2938	-0.6792	0.4915	-2.7000	0.1562	-1.4080	1.0500	0.5477	-0.8977	1.4860	-0.2971	-0.5299	0.4005	0.2532	1.4420	0.3289	-0.1665	-0.6019	0.0000	0.1225	0.6300	-0.2422	0.3357	-1.2510	0.1316	0.7018	0.0000	0.3027	0.6011	-0.5173	-0.5995	0.6047	-0.2077	0.2008	-1.2640	1.7660	0.3190	-0.6460	0.1444	-0.1706	0.0000	0.4796	1.0180	-1.4320	1.4450	-0.2960	1.3160	-0.3709	0.0000	-1.6110	0.3017	0.4162	-0.0589	-1.2810	-0.5646	1.3920	0.2573	-1.8630	-1.4060	-0.8639	-0.4986	0.9745	1.4920	0.3711	0.0279	0.6801	-0.1431	1.0410	0.3855	0.7553	1.6860	0.4932	-0.2651	1.1830	0.9738	0.5241	0.3419	-0.5831	-0.2508	-1.3410	-1.1640	-0.1668	-0.0342	0.8249	-0.0131	0.0000	0.9043	0.1662	-0.3453	1.3360	-0.3655	-0.6961	-0.4308	0.0000	0.7822	-0.6745	0.8924	-0.2111	0.7271	1.6740	2.2850	-0.6745	-0.4634	-1.6900	-0.3649	-0.9032	-0.1633	-1.3880	-1.1370	-1.2700	-0.6423	-1.3100	-1.6090	0.9920	1.1970	1.1150	-0.9618	-0.3988	-0.0109	0.3399	-0.5405	1.1190	0.6789	0.3917	-1.9460	-0.2215	-0.1782	-1.0270	-0.4773	-1.5490	-0.9117	-0.7926	-1.1970	0.8247	0.4431	1.4850	-0.0666	0.7227	-0.8094	0.4121	0.2566	1.0350	-0.6698	-1.0020	-0.7150	0.9726	-0.0550	-0.8861	0.7264	1.0160	0.4907	1.0350	0.1357	0.5929	-1.7070	-0.3966	-10.0000	0.0472	0.5929	1.3980	0.8074	-1.6370	0.8040	-1.6680	0.9047	1.9140	-2.1710	1.4720	0.0429	0.7096	-0.4162	0.7050	-0.3331	0.3867	-0.5725	0.9043	-0.4639	1.0570	0.2984	-0.1268	0.8745	-0.3670	-0.6745	0.2748	0.8101	1.7040	-0.3871	-0.6745	-0.7185	0.0000	-0.3093	0.0614	-1.0190	0.9962	-0.9680	1.4920	0.2381	-0.0645	-0.2846	1.3330	0.0000	0.6653	0.1693	0.0000	-0.4773	0.0743	0.4294	-0.8562	0.9180	0.4062	0.2048	-0.9851	-1.1220	0.9106	-0.2094	1.1270	0.1874	-0.0465	1.0560	-0.6324	-0.3651	0.9485	-0.6745	0.0479	-1.0470	-1.2440	-0.7675	0.2327	0.3698	1.4900	0.3632	1.4380	-0.5047	-0.2092	-0.4386	-1.1780	0.8236	1.0540	0.6745	-0.6354	-0.6745	-1.3780	0.7521	-0.9456	-0.6904	2.0540	-0.3131	-0.0809	0.3910	1.7660	-1.0020	-0.7534	0.5000	-0.6269	-0.3774	0.7364	-0.1659	0.2341	1.0060	0.3204	-0.0852	-0.2284	-0.2533	-0.3174	0.9642	-0.0853	0.1559	0.1419	0.1197	0.6774	0.1332	-0.5176	0.1232	0.0827	0.6995	0.5953	1.0600	-0.4255	0.8624	1.0010	0.0840	-0.6553	-0.1093	0.0587	0.5490	0.6331	0.5227	0.2186	0.2781	0.8947	-0.0202	0.5966	0.2249	0.5671	0.6382	-0.3909	0.7040	0.1462	0.0655	-0.5605	0.5903	-0.2353	-0.1285	-0.1740	0.3559	0.0204	0.3385	-0.2290	-0.3813	-0.0856	1.1080	-0.4190	0.8741	0.9074	-0.2274	0.3215	0.1535	-0.4640	-0.5943	0.3973	0.1500	0.5178	0.5159	0.6091	0.1813	-0.4249	0.7832	0.6529	0.5648	0.4817	0.0587	0.5303	0.6376	-0.3966	-1.4950	-0.9625	-0.0541	0.6273	0.4563	0.0698	0.8134	0.1924	0.6054	-0.1824	0.0042	0.0048	0.6670	1.0690	0.5523	-0.3031	0.1094	0.2885	-0.3786	0.7125

Features g- signify gene expression data, and c- signify cell viability data. cp_type indicates samples treated with a compound (cp_vehicle) or with a control perturbation (ctrl_vehicle); control perturbations have no MoAs; cp_time and cp_dose indicate treatment duration (24, 48, 72 hours) and dose (high or low).

To get the corresponding known MOA notation of each drug, I use Dr.Insight, which is a useful package in R that provides pathway analysis where the mechanisms of identified drugs are investigated at pathway level. Since DepMap and Dr.Insight both have the NCI pathway interaction database (PID) embeded, calling

#path.analysis.res = pathway.analysis(drug.ident.res = drug.ident.res, pathway.list =pathway.PID, pathway.list.path = NULL, drug.FDR.cutoff = 0.1)

and rearranging by setting drug-pathway adjacency table with values ‘1’, ‘0’ ‘-1’ (where ‘1’ means a drug up-regulates a pathway, ‘-1’ means down-regulation and ‘0’ means no significant regulation relationship), I got the training and target dataset.

train_target = pd.read_csv('train_targets_scored.csv')
train_features,test_features,train_target,test_target=train_test_split(train_features,train_target,test_size=0.2)
df = pd.concat([train_features, test_features])

test_features.head()

	sig_id	cp_type	cp_time	cp_dose	g-0	g-1	g-2	g-3	g-4	g-5	g-6	g-7	g-8	g-9	g-10	g-11	g-12	g-13	g-14	g-15	g-16	g-17	g-18	g-19	g-20	g-21	g-22	g-23	g-24	g-25	g-26	g-27	g-28	g-29	g-30	g-31	g-32	g-33	g-34	g-35	g-36	g-37	g-38	g-39	g-40	g-41	g-42	g-43	g-44	g-45	g-46	g-47	g-48	g-49	g-50	g-51	g-52	g-53	g-54	g-55	g-56	g-57	g-58	g-59	g-60	g-61	g-62	g-63	g-64	g-65	g-66	g-67	g-68	g-69	g-70	g-71	g-72	g-73	g-74	g-75	g-76	g-77	g-78	g-79	g-80	g-81	g-82	g-83	g-84	g-85	g-86	g-87	g-88	g-89	g-90	g-91	g-92	g-93	g-94	g-95	g-96	g-97	g-98	g-99	g-100	g-101	g-102	g-103	g-104	g-105	g-106	g-107	g-108	g-109	g-110	g-111	g-112	g-113	g-114	g-115	g-116	g-117	g-118	g-119	g-120	g-121	g-122	g-123	g-124	g-125	g-126	g-127	g-128	g-129	g-130	g-131	g-132	g-133	g-134	g-135	g-136	g-137	g-138	g-139	g-140	g-141	g-142	g-143	g-144	g-145	g-146	g-147	g-148	g-149	g-150	g-151	g-152	g-153	g-154	g-155	g-156	g-157	g-158	g-159	g-160	g-161	g-162	g-163	g-164	g-165	g-166	g-167	g-168	g-169	g-170	g-171	g-172	g-173	g-174	g-175	g-176	g-177	g-178	g-179	g-180	g-181	g-182	g-183	g-184	g-185	g-186	g-187	g-188	g-189	g-190	g-191	g-192	g-193	g-194	g-195	g-196	g-197	g-198	g-199	g-200	g-201	g-202	g-203	g-204	g-205	g-206	g-207	g-208	g-209	g-210	g-211	g-212	g-213	g-214	g-215	g-216	g-217	g-218	g-219	g-220	g-221	g-222	g-223	g-224	g-225	g-226	g-227	g-228	g-229	g-230	g-231	g-232	g-233	g-234	g-235	g-236	g-237	g-238	g-239	g-240	g-241	g-242	g-243	g-244	g-245	g-246	g-247	g-248	g-249	g-250	g-251	g-252	g-253	g-254	g-255	g-256	g-257	g-258	g-259	g-260	g-261	g-262	g-263	g-264	g-265	g-266	g-267	g-268	g-269	g-270	g-271	g-272	g-273	g-274	g-275	g-276	g-277	g-278	g-279	g-280	g-281	g-282	g-283	g-284	g-285	g-286	g-287	g-288	g-289	g-290	g-291	g-292	g-293	g-294	g-295	g-296	g-297	g-298	g-299	g-300	g-301	g-302	g-303	g-304	g-305	g-306	g-307	g-308	g-309	g-310	g-311	g-312	g-313	g-314	g-315	g-316	g-317	g-318	g-319	g-320	g-321	g-322	g-323	g-324	g-325	g-326	g-327	g-328	g-329	g-330	g-331	g-332	g-333	g-334	g-335	g-336	g-337	g-338	g-339	g-340	g-341	g-342	g-343	g-344	g-345	g-346	g-347	g-348	g-349	g-350	g-351	g-352	g-353	g-354	g-355	g-356	g-357	g-358	g-359	g-360	g-361	g-362	g-363	g-364	g-365	g-366	g-367	g-368	g-369	g-370	g-371	g-372	g-373	g-374	g-375	g-376	g-377	g-378	g-379	g-380	g-381	g-382	g-383	g-384	g-385	g-386	g-387	g-388	g-389	g-390	g-391	g-392	g-393	g-394	g-395	g-396	g-397	g-398	g-399	g-400	g-401	g-402	g-403	g-404	g-405	g-406	g-407	g-408	g-409	g-410	g-411	g-412	g-413	g-414	g-415	g-416	g-417	g-418	g-419	g-420	g-421	g-422	g-423	g-424	g-425	g-426	g-427	g-428	g-429	g-430	g-431	g-432	g-433	g-434	g-435	g-436	g-437	g-438	g-439	g-440	g-441	g-442	g-443	g-444	g-445	g-446	g-447	g-448	g-449	g-450	g-451	g-452	g-453	g-454	g-455	g-456	g-457	g-458	g-459	g-460	g-461	g-462	g-463	g-464	g-465	g-466	g-467	g-468	g-469	g-470	g-471	g-472	g-473	g-474	g-475	g-476	g-477	g-478	g-479	g-480	g-481	g-482	g-483	g-484	g-485	g-486	g-487	g-488	g-489	g-490	g-491	g-492	g-493	g-494	g-495	g-496	g-497	g-498	g-499	g-500	g-501	g-502	g-503	g-504	g-505	g-506	g-507	g-508	g-509	g-510	g-511	g-512	g-513	g-514	g-515	g-516	g-517	g-518	g-519	g-520	g-521	g-522	g-523	g-524	g-525	g-526	g-527	g-528	g-529	g-530	g-531	g-532	g-533	g-534	g-535	g-536	g-537	g-538	g-539	g-540	g-541	g-542	g-543	g-544	g-545	g-546	g-547	g-548	g-549	g-550	g-551	g-552	g-553	g-554	g-555	g-556	g-557	g-558	g-559	g-560	g-561	g-562	g-563	g-564	g-565	g-566	g-567	g-568	g-569	g-570	g-571	g-572	g-573	g-574	g-575	g-576	g-577	g-578	g-579	g-580	g-581	g-582	g-583	g-584	g-585	g-586	g-587	g-588	g-589	g-590	g-591	g-592	g-593	g-594	g-595	g-596	g-597	g-598	g-599	g-600	g-601	g-602	g-603	g-604	g-605	g-606	g-607	g-608	g-609	g-610	g-611	g-612	g-613	g-614	g-615	g-616	g-617	g-618	g-619	g-620	g-621	g-622	g-623	g-624	g-625	g-626	g-627	g-628	g-629	g-630	g-631	g-632	g-633	g-634	g-635	g-636	g-637	g-638	g-639	g-640	g-641	g-642	g-643	g-644	g-645	g-646	g-647	g-648	g-649	g-650	g-651	g-652	g-653	g-654	g-655	g-656	g-657	g-658	g-659	g-660	g-661	g-662	g-663	g-664	g-665	g-666	g-667	g-668	g-669	g-670	g-671	g-672	g-673	g-674	g-675	g-676	g-677	g-678	g-679	g-680	g-681	g-682	g-683	g-684	g-685	g-686	g-687	g-688	g-689	g-690	g-691	g-692	g-693	g-694	g-695	g-696	g-697	g-698	g-699	g-700	g-701	g-702	g-703	g-704	g-705	g-706	g-707	g-708	g-709	g-710	g-711	g-712	g-713	g-714	g-715	g-716	g-717	g-718	g-719	g-720	g-721	g-722	g-723	g-724	g-725	g-726	g-727	g-728	g-729	g-730	g-731	g-732	g-733	g-734	g-735	g-736	g-737	g-738	g-739	g-740	g-741	g-742	g-743	g-744	g-745	g-746	g-747	g-748	g-749	g-750	g-751	g-752	g-753	g-754	g-755	g-756	g-757	g-758	g-759	g-760	g-761	g-762	g-763	g-764	g-765	g-766	g-767	g-768	g-769	g-770	g-771	c-0	c-1	c-2	c-3	c-4	c-5	c-6	c-7	c-8	c-9	c-10	c-11	c-12	c-13	c-14	c-15	c-16	c-17	c-18	c-19	c-20	c-21	c-22	c-23	c-24	c-25	c-26	c-27	c-28	c-29	c-30	c-31	c-32	c-33	c-34	c-35	c-36	c-37	c-38	c-39	c-40	c-41	c-42	c-43	c-44	c-45	c-46	c-47	c-48	c-49	c-50	c-51	c-52	c-53	c-54	c-55	c-56	c-57	c-58	c-59	c-60	c-61	c-62	c-63	c-64	c-65	c-66	c-67	c-68	c-69	c-70	c-71	c-72	c-73	c-74	c-75	c-76	c-77	c-78	c-79	c-80	c-81	c-82	c-83	c-84	c-85	c-86	c-87	c-88	c-89	c-90	c-91	c-92	c-93	c-94	c-95	c-96	c-97	c-98	c-99
3383	id_24647064e	ctl_vehicle	48	D1	-0.6660	-0.5623	-0.5062	1.3060	-0.1963	-0.9721	0.3686	-0.8863	0.4302	2.1590	1.1510	-0.8814	-0.0105	0.7462	0.5334	0.7018	0.3639	0.8036	-0.4219	-0.0679	1.0540	-0.1986	-0.4418	-0.1805	0.1920	-0.5977	0.1217	0.9742	0.5312	0.1736	0.8788	-0.3401	-0.5113	-0.5301	0.3517	-0.8194	0.0506	0.0123	0.2527	0.0809	0.7966	0.4461	-0.1623	-1.6330	1.7940	1.1540	-0.7710	0.4322	1.0610	0.8001	0.8510	0.3158	-1.1940	0.8474	-0.4549	0.3782	0.0092	1.8400	0.7288	-0.4543	0.4633	-0.3566	-0.8650	-2.5770	-0.3539	-1.0810	0.7314	0.0249	-1.2900	0.2039	0.2045	1.0700	-0.1020	-0.2340	0.8643	1.0350	0.2681	-0.2841	0.5092	-0.1892	-0.0765	0.0486	-0.5925	0.2103	0.2102	-0.5710	0.8464	-0.1058	1.3360	-0.1644	-0.3079	0.1464	0.2517	2.1720	-1.3000	0.5220	0.0843	-0.7559	-0.1017	1.0600	-1.9360	-0.1825	0.5863	0.1060	-0.3875	1.8610	-0.2866	0.5304	0.7675	-1.5820	0.5264	0.7230	-0.9645	1.0020	0.8775	-0.1171	-0.9958	0.0906	0.6421	-1.3420	-1.4130	0.6813	-0.2783	-0.0673	-0.4842	-0.0693	-0.2886	-0.2465	-0.6945	-2.2310	-0.9217	0.3298	0.5875	2.1160	-0.8528	0.4060	1.2650	-0.2682	0.2270	0.4792	0.4624	-0.4446	0.7944	0.2110	0.6841	-0.3024	-0.0921	0.7250	0.9878	0.7236	0.6665	0.0590	1.1630	-1.4240	-0.6961	0.1830	-0.1581	0.2792	-0.5916	0.1745	0.0862	1.2440	-0.1167	0.5936	-0.8584	-0.6071	0.0638	1.4670	0.4533	-1.2220	1.0450	-0.1629	-0.6361	-0.4534	-0.5415	-0.0289	0.5590	1.1280	0.3374	1.2780	0.0736	-0.9177	-0.0866	1.8560	-0.1854	0.6488	1.0080	0.3749	0.9378	0.0823	0.4294	0.1300	0.2325	-1.1220	0.0593	-1.0870	0.6004	-0.3185	0.4770	1.0870	0.6206	0.6220	0.7078	0.4909	0.8044	-0.3582	0.0953	-1.0770	0.0628	0.0446	0.3004	0.9076	-1.9340	0.0671	0.5028	0.7079	-1.1510	-1.5660	0.6421	0.4297	0.2946	-0.5376	-0.9001	0.3982	1.1650	-0.0398	-0.2515	-0.0587	0.4777	-1.1990	-0.4303	-0.3327	-1.1790	-0.4054	-0.9766	-0.0711	0.3435	0.0000	0.3241	-2.5240	0.0689	-0.2788	-1.0780	0.7728	0.6563	-0.3814	1.1410	-0.7326	1.2900	0.1753	0.7333	-1.4760	0.9912	1.0140	1.0950	0.4251	0.1409	1.3670	1.2450	0.4096	-1.1800	-1.4960	-0.3956	0.4467	-0.7259	-0.8834	0.3036	1.0040	-0.6378	1.4610	0.2290	-0.4228	-0.1204	0.7501	0.1005	-0.7145	-0.3932	-1.1720	0.1933	0.5857	-0.1387	0.5601	1.1580	0.1547	0.8024	-0.1306	-1.8740	0.0034	0.9301	0.3635	-0.1387	0.8513	0.5578	1.5420	0.8477	1.0020	-0.4114	-0.5678	1.3280	0.5716	1.0200	0.6906	1.0240	-0.7630	1.0350	0.3669	-0.9324	-0.2654	0.9764	-0.4126	1.4500	-2.0770	-0.0395	1.1930	0.9000	-1.0740	-0.5854	0.1484	-1.0360	0.5347	1.0400	0.1244	0.2402	-0.1915	0.0590	-0.6214	-0.1856	0.6414	-1.2510	0.5763	-1.0400	0.1268	-0.4702	-1.7540	0.3424	0.2330	-2.1660	-0.0403	0.0951	-0.6534	0.7822	-0.2370	0.4344	0.3666	0.3591	1.1810	-0.2481	1.1340	0.2777	0.3740	0.4747	-0.0460	-0.7696	0.6206	0.8611	0.4129	-0.9983	1.0360	0.4336	0.1711	0.0159	-0.8430	0.5467	-0.5871	1.1830	-1.9790	0.5585	-0.4533	0.2762	0.9381	0.2654	1.4300	1.0040	-0.0442	-0.5060	0.0632	0.1703	-1.2310	0.0387	-0.4650	-0.2979	-0.3152	-2.0480	-0.4590	1.4610	-1.2440	0.1319	0.3826	0.3206	0.8413	0.9802	1.2510	0.5086	0.1875	-0.0621	1.1350	0.6122	-1.1880	-0.5868	-2.7040	1.1050	-0.5739	0.6248	-1.1930	-0.9150	0.4035	1.7580	-0.3989	0.2996	-0.1668	1.4070	0.8037	-0.3618	0.2785	0.9586	0.3209	-0.3061	-0.5008	-1.3510	0.0003	0.6186	-1.5360	-0.8297	1.1080	0.4972	-0.0457	-0.6496	-0.7072	-0.3147	-1.0530	-0.1262	-0.9322	0.0382	0.1692	-0.1113	1.1490	0.7038	0.8797	-0.5083	1.1440	-1.6020	0.4617	1.7790	-0.5784	0.3022	-0.0717	-0.0135	-1.2880	0.4830	-0.4455	-0.7495	1.1270	0.1757	-0.2210	-1.8750	0.6770	0.9233	-1.7890	1.3090	1.6760	0.3224	-0.0420	-0.2697	0.6307	-0.2440	1.2270	-0.7081	0.1170	-0.5644	0.2936	-0.4988	-2.1540	-1.5580	0.7309	-0.4546	0.0485	1.2640	0.2693	-0.2919	-0.1944	0.5793	-0.1038	0.8053	-1.2910	-0.6202	0.1137	-0.8018	-0.4002	0.8402	1.5440	-0.7341	1.1250	0.3417	-0.4835	-0.6076	0.1096	0.1061	0.2591	0.8874	-0.8462	0.1810	0.0265	0.3580	-0.1562	-1.6870	0.8687	0.6974	1.6650	-0.0001	0.2097	1.8270	0.8515	0.3206	-1.1280	-0.3275	0.4085	0.0614	1.2290	-0.0823	0.4365	1.7770	-1.0970	0.1608	0.2915	0.7908	-0.5041	-0.2032	-0.8128	0.5599	0.5537	0.3102	1.2010	-0.0312	-0.0332	0.0621	0.5613	-0.5088	0.6265	0.4003	-0.2993	-0.7164	-0.0131	0.6359	0.6978	-0.1168	1.2360	-0.4823	0.7872	-0.1670	0.3596	-1.0580	0.0585	0.1039	-1.2870	-0.2054	-0.5878	-0.9798	-0.1711	0.0442	-1.0050	-0.1455	1.3160	0.5852	0.5749	0.2090	0.4345	-1.2940	0.0650	-1.0120	0.8648	-0.4339	0.8666	0.3563	0.0003	-0.3857	1.3260	-0.0398	-1.1300	0.4999	0.3323	-0.0003	0.8414	-0.6449	1.0610	0.1500	-0.7810	-0.8400	0.1220	0.5194	0.5460	0.5641	0.7466	-0.2422	-0.2072	0.4138	-1.7980	-0.4563	0.0545	0.5992	-0.7673	-0.2408	0.9716	0.2716	-0.4431	-0.6834	0.0575	0.6484	-0.8608	-0.7356	0.4380	0.7667	-1.4080	0.3512	-0.5299	0.1527	0.1057	-0.1570	0.2584	-0.7372	0.1268	-2.0960	0.6425	-0.4374	-0.4030	-0.6217	0.5628	-0.1587	-0.1766	0.7631	0.3070	-0.9172	-0.3042	0.2210	0.4750	-0.8611	-0.7324	-0.0362	-0.0291	-0.1247	0.8460	1.1190	1.6070	0.7738	-0.6143	1.1980	0.5293	-0.2239	-1.6380	0.4912	-0.3744	-2.4350	0.4385	1.0660	-0.2603	-1.3620	-0.9581	0.0752	1.5750	0.0600	2.0120	0.2992	-0.8572	1.2730	0.4433	0.5297	-0.4600	-0.1328	1.2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50	-0.8036	-0.1036	0.6344	1.0830	-0.1525	-0.4289	-0.3546	0.0539	-0.0464	-0.5510	-0.4451	-0.3127	-0.5192	-0.6769	1.4120	0.0000	-0.0622	-0.8420	-0.0742	0.4490	0.2113	0.0982	-1.0200	0.0012	0.3455	-0.1126	0.2911	0.5331	-0.1080	0.4958	-0.3135	0.8036	0.3637	-0.8458	2.1780	-0.4487	0.6502	0.3400	-0.7416	0.6121	-1.1290	1.1730	1.1450	-1.2620	0.4541	2.817	0.0559	-0.0607	0.1648	0.3625	0.4066	0.0315	0.0728	-0.7854	0.3016	-0.4293	-0.2731	-0.6234	-0.0261	0.7492	0.2910	-0.2691	-0.5178	0.1207	0.2384	0.3318	-0.9387	-0.5290	0.6435	-0.0043	0.2014	0.5109	-0.6923	0.7592	-0.7806	0.0500	-1.1610	-0.2226	-0.4372	0.0340	-0.6044	-0.4093	-1.0100	-0.4089	-1.0450	0.7070	3.5060	-0.3995	0.0222	-0.1661	-0.0143	-1.3070	0.4030	-5.2950	0.3946	-0.7293	0.7737	0.4132	1.1890	0.5553	0.6283	-0.7500	-0.1583	-0.0065	0.5619	0.3600	0.9998	-0.7095	-0.0834	0.3775	-0.7546	-0.0725	-0.1127	0.3042	1.2440	-0.9171	0.4061	-0.0762	-0.1202	0.7588	0.8713	-0.5173	-0.1132	0.6746	0.1210	0.3209	-0.5375	0.5403	-0.2410	0.4687	1.4460	0.0932	0.3055	-0.0677	1.2320	0.6363	-0.3219	0.9811	-0.5315	-0.6341	0.6599	0.8305	0.6980	-0.5833	1.0440	1.2380	0.3779	-1.2530	0.8815	0.4094	0.6375	0.5093	0.2352	-0.4693	0.7223	-0.1072	1.1800	0.7563	0.6219	-1.1010	0.6622	1.1110	0.1456	0.0328	-0.0536	-0.0756	0.8765	0.5196	-1.7190	-0.1546	0.5878	-0.5061	1.8340	0.8986	0.4448	-0.0403	0.2947	-0.2858	0.6945	-1.1320	-0.8691	0.3312	0.1855	0.5107	0.5913	-1.1480	0.3754	0.2542	1.0600	0.3137	-0.1828	-0.6219	0.2940	-0.4196	0.9729	0.8715	1.0660	0.4728	-0.9362	0.5893	-0.1984	1.3970	0.4147	-0.0898	-0.1053	0.3646	0.2448	-0.2511	0.0470	-1.7940
15281	id_a446e2e51	trt_cp	72	D2	-0.3441	-0.1275	0.3607	0.7475	-0.1539	0.7183	0.0922	-0.1953	-0.4663	-0.4277	0.0367	0.7712	-0.2950	0.4957	0.2069	-0.2880	0.0215	-0.3371	0.5373	-0.2186	-0.3499	-0.1038	1.2160	0.4047	0.6201	-0.4712	0.7485	1.0790	-0.4664	-0.6000	1.0250	-0.5841	0.7786	-0.1075	0.1936	-0.4611	-0.2651	-0.0023	-0.0066	-0.4367	0.2564	-0.3426	-0.2763	-0.3065	0.0798	-0.5579	-0.5828	0.3855	1.2100	-0.5971	-0.0215	-0.3791	-0.1700	0.5337	0.8857	0.0815	0.5261	0.1761	-1.0080	0.0743	0.5061	0.4665	0.9283	-0.7755	-0.2513	0.6000	-0.3266	0.7312	0.6154	0.5835	0.3673	0.3123	-0.1325	-0.1052	-0.5311	0.5750	-0.0722	0.0926	-0.0539	-0.5746	-0.1048	-0.5343	-0.6040	-0.5491	0.2670	-0.4199	-0.3004	-0.1154	-0.2036	0.5483	0.7270	-0.6188	-0.0974	-0.4349	0.4963	-1.2310	-1.5310	0.0537	0.7898	-0.8707	0.1889	-0.4788	-0.3167	0.1752	-0.3372	-0.4689	-0.1933	0.5139	0.1638	0.5863	-0.4189	-0.4083	-0.2557	0.1828	-0.3564	-0.4401	-0.2838	0.4609	-0.0290	-0.0932	-0.4577	0.5689	-0.2041	-0.2560	0.3437	0.5580	-2.2810	0.5197	0.2415	0.1488	-1.0730	-1.0830	-0.5584	0.0294	0.4285	-0.6331	-0.3681	1.4900	-0.2034	-0.8404	-0.1693	0.2244	-0.1636	-0.3915	0.0910	0.2837	-0.0003	-0.1872	-0.1273	0.7282	-0.3207	0.4787	0.6012	-0.7041	0.6040	-0.0732	-0.1287	0.8327	0.0916	0.3929	1.4760	-1.7120	-0.4906	-0.4461	-0.0394	-0.3409	0.1347	0.3247	0.1804	0.6538	0.7127	-0.4697	-0.7022	0.3029	-0.2037	0.1441	0.1468	-0.2957	0.1121	0.3504	0.0198	-0.1182	-0.7595	0.3911	-0.0783	0.1587	-0.5082	-0.6782	0.9248	-0.0088	-0.4791	1.6830	-0.2782	0.2580	0.4381	0.3798	0.0100	-0.3929	0.1704	-0.1641	0.1191	-0.2449	0.0858	-0.4215	0.0036	-0.5355	-0.3361	-1.1570	-1.0400	-0.7025	0.1618	0.0030	0.1052	0.0000	-0.3534	0.4078	0.0510	0.4585	0.1785	0.3018	-0.6105	-0.3824	0.8687	-0.6511	-1.0400	0.2409	0.6247	-0.8795	0.3115	-0.6582	0.2806	-0.9139	-0.5371	-0.4363	0.0451	0.4709	-0.1062	-0.4057	-0.5538	0.1228	-0.2689	0.2379	-0.0719	-0.6194	-0.9171	0.0213	0.0819	-0.0418	0.1344	-0.1581	-0.8226	0.4633	0.0072	-0.4891	0.1288	-0.3693	0.6126	-0.0510	-1.1830	0.0481	0.0425	1.0140	0.7632	-0.2674	-0.0182	0.3778	-0.4809	-0.1133	0.2226	0.1397	0.4839	0.0775	-0.4370	-0.6109	-0.3841	0.3914	0.8549	0.5610	-0.2923	-0.2337	-0.3495	-1.4770	-0.2765	0.3602	-0.2397	-0.1824	-0.2185	-0.1397	0.4051	0.0004	0.2194	0.4705	-0.3042	0.2022	0.1590	0.0796	0.7377	0.0080	0.2612	-0.2600	0.3144	-0.3547	-1.0880	0.0644	-0.0663	-0.3257	0.0881	0.0000	1.3010	0.3162	0.3321	-0.3322	-1.2960	0.4622	-0.0801	0.1613	-0.4861	0.1639	-0.5798	-0.2358	0.3591	0.3029	0.5124	-0.4961	-1.0970	-0.1746	0.0612	0.1461	0.4908	-0.1002	-0.3925	-0.5250	0.0175	0.1083	0.4193	-0.2684	-0.0780	-0.5891	-0.3481	-0.8032	-0.2269	-0.2828	0.7837	-0.8298	0.7495	0.5016	-0.5896	0.2149	0.2699	-0.8932	-0.5296	0.0620	-0.4323	0.6994	-0.7736	0.4679	-0.3020	0.3306	2.0270	-0.8152	-0.0857	-0.3202	-0.3992	-0.5275	0.4388	0.3449	0.6414	-0.2199	-0.7352	0.3632	-4.4070	0.8165	0.4025	-0.2016	-0.0796	0.2599	-0.5773	-0.7343	0.4017	0.6021	0.2718	-0.4487	-0.5950	-0.7303	-0.0442	-0.8929	-0.4627	0.0522	1.0180	0.0662	-0.1921	-0.5812	-0.2338	0.0505	-0.4078	0.0811	-0.2314	0.5116	0.3918	-0.5379	-0.0253	-0.6862	-0.2666	0.2256	-0.0365	0.6137	0.3738	0.1181	0.1757	-0.1401	0.5060	0.0128	0.4363	0.1014	-0.2656	0.2392	0.1289	0.1187	-0.3119	0.1824	0.2573	0.4615	0.0522	0.6327	0.0004	-0.2123	0.4157	-0.1673	0.2970	0.9515	1.1470	0.6662	0.4151	0.3721	0.4780	0.0000	-0.5855	-0.4969	-0.1970	0.2303	-2.0290	-0.1228	0.1328	-0.0525	0.0646	-0.8292	0.0000	-0.4001	-0.0557	-0.4096	-1.3310	0.3000	-0.4894	0.0614	-0.5227	0.3905	0.5304	0.5267	-0.6652	0.0781	0.1838	-0.1018	0.1818	0.0215	-0.5777	-0.2928	-0.1249	0.3515	0.2344	0.4882	0.5254	0.7372	0.0300	0.1639	-0.8882	-0.8953	0.6071	0.4321	-0.4229	0.5318	-0.3626	0.1949	0.8350	-1.6070	-0.0970	0.0454	-0.3246	0.4726	-0.8127	0.3144	-0.2640	-0.8012	-0.5389	0.5990	-0.3198	-0.5360	-0.3285	-0.2028	0.0845	-0.2224	0.3447	0.4577	-0.5013	0.6057	-0.2020	2.0170	-0.6465	-0.4212	0.6313	0.0625	-0.2630	-0.0291	0.7251	-0.0549	-0.0143	0.8264	0.1921	0.5864	-0.3112	0.0016	-0.5789	0.3359	-0.4909	-0.4422	0.4598	0.7996	-0.0466	1.3000	-1.5940	-0.0487	-0.5333	-0.3357	0.5554	0.1533	-0.1552	-0.0887	-1.2900	-2.5390	0.2079	-0.0670	0.3973	-0.5367	-0.4108	-0.0018	0.4140	-0.3841	-0.3397	-0.5740	-0.0964	0.2524	0.1636	0.0003	0.4534	0.9675	0.1224	0.2421	-0.5443	0.1807	0.7668	-0.1293	0.3664	0.2114	1.3990	0.4633	0.3479	3.2050	-0.3652	-0.1145	0.5482	-0.0057	-0.9511	-2.2850	-0.4986	-0.4997	0.1524	-0.4331	-0.5843	0.6079	0.8839	0.0752	0.7499	0.1276	1.1370	-0.2062	0.4522	0.0459	1.0760	0.4302	0.8402	0.5914	0.1928	-0.9388	0.3597	-0.5891	-0.6651	0.0194	0.9403	-0.0559	-1.5110	5.9330	-0.4627	-0.2244	-0.1959	-0.0804	0.0509	0.8957	-0.1053	-0.1241	-0.3712	-0.5602	0.4804	-0.0226	-0.0757	0.2645	-0.3788	0.3663	0.5723	0.0733	0.3510	-0.6411	0.6047	-1.2500	-0.0520	-0.1140	-0.0616	0.6740	0.0000	-0.3907	0.1295	-0.3940	0.2266	0.1279	0.6216	-0.3011	0.1504	-0.5275	0.2547	0.2782	0.3093	0.7888	0.0519	-0.4104	0.2833	0.0641	0.3730	-0.2907	-0.9391	-0.2901	-0.9431	0.5551	-1.4030	-0.1292	-0.4465	0.7018	0.1882	0.1821	0.7797	-0.2213	-0.4695	-0.0284	-1.3800	-0.6401	-0.5150	0.6342	-0.5955	-0.3462	1.1820	0.6644	0.6039	0.7312	0.0802	-0.0450	0.7804	-0.0905	-0.4266	-0.5962	0.1991	-0.1649	0.7167	-0.7276	-0.1892	-0.3201	0.3210	0.5382	-0.9961	0.4867	-0.1026	0.5605	0.1096	0.4338	-0.8765	-0.1592	-0.0763	0.1179	0.4358	0.1924	-0.4648	0.4702	-0.1834	0.3224	-0.6320	0.2130	-1.2720	0.0465	0.3011	0.1289	-0.2073	3.939	-0.2270	-0.3318	-0.3191	-0.2980	-0.3027	1.8490	1.6820	-0.8368	0.2993	0.9402	0.2435	1.2130	0.1877	4.9100	-0.2001	-0.0167	0.5358	-0.5047	0.2922	-0.4036	-0.3927	-0.0919	-0.0398	-0.2263	0.6078	0.3384	-0.6191	-0.0966	-0.6271	2.5910	0.0328	0.3543	0.3688	0.5708	0.5568	-0.2823	-0.6632	-1.0090	1.7320	0.7428	0.4870	0.5318	-0.1211	0.3307	-0.6278	-0.1254	-0.1376	-0.5167	0.1447	-0.7935	-0.1798	-0.4897	-0.7289	0.3246	0.6815	0.5927	0.0089	0.8307	0.2607	-0.3392	-0.2492	0.3210	0.0300	0.2686	-0.0608	0.4914	-0.1098	0.1828	-0.7101	-0.2754	-0.1185	0.0471	-0.4872	-0.1304	0.4320	-1.8060	0.3774	0.1630	0.7390	0.3491	-1.0450	0.5709	-0.0198	-0.6701	0.4652	-0.5870	0.4687	0.1606	-1.4170	-1.2360	-0.4067	-0.1552	0.1202	0.3632	-0.4292	-0.3387	0.0832	0.5666	-1.0240	-0.5304	0.4099	-0.5861	-0.5648	-0.2056	0.4037	-0.0491	0.1681	0.0504	-0.6480	1.0250	0.3339	0.1530	-0.0869	-0.2055	0.9261	0.1252	0.0232	0.0670	0.2354	-0.5983	-0.5939	0.0371	-1.3130	-0.2284	-0.8324	0.1385	-0.1683	0.0735	0.1188	0.5304	0.1905	-0.1978	-0.7382	-0.7211	0.1704	0.0784	-0.0742	0.2983	1.1890	-1.4200	-0.1427	-0.2121	-0.0990	0.3888	-0.4457	0.2857	0.3261	-0.4874	-1.2320	0.0876	-0.0888	-0.3422	-0.2700	-0.2479	-0.2720	0.7778	-0.1273	-0.4196	-0.0674	0.3516	0.2311	0.3542	-0.1559	0.6924

train_target.head()

	sig_id	5-alpha_reductase_inhibitor	11-beta-hsd1_inhibitor	acat_inhibitor	acetylcholine_receptor_agonist	acetylcholine_receptor_antagonist	acetylcholinesterase_inhibitor	adenosine_receptor_agonist	adenosine_receptor_antagonist	adenylyl_cyclase_activator	adrenergic_receptor_agonist	adrenergic_receptor_antagonist	akt_inhibitor	aldehyde_dehydrogenase_inhibitor	alk_inhibitor	ampk_activator	analgesic	androgen_receptor_agonist	androgen_receptor_antagonist	anesthetic_-_local	angiogenesis_inhibitor	angiotensin_receptor_antagonist	anti-inflammatory	antiarrhythmic	antibiotic	anticonvulsant	antifungal	antihistamine	antimalarial	antioxidant	antiprotozoal	antiviral	apoptosis_stimulant	aromatase_inhibitor	atm_kinase_inhibitor	atp-sensitive_potassium_channel_antagonist	atp_synthase_inhibitor	atpase_inhibitor	atr_kinase_inhibitor	aurora_kinase_inhibitor	autotaxin_inhibitor	bacterial_30s_ribosomal_subunit_inhibitor	bacterial_50s_ribosomal_subunit_inhibitor	bacterial_antifolate	bacterial_cell_wall_synthesis_inhibitor	bacterial_dna_gyrase_inhibitor	bacterial_dna_inhibitor	bacterial_membrane_integrity_inhibitor	bcl_inhibitor	bcr-abl_inhibitor	benzodiazepine_receptor_agonist	beta_amyloid_inhibitor	bromodomain_inhibitor	btk_inhibitor	calcineurin_inhibitor	calcium_channel_blocker	cannabinoid_receptor_agonist	cannabinoid_receptor_antagonist	carbonic_anhydrase_inhibitor	casein_kinase_inhibitor	caspase_activator	catechol_o_methyltransferase_inhibitor	cc_chemokine_receptor_antagonist	cck_receptor_antagonist	cdk_inhibitor	chelating_agent	chk_inhibitor	chloride_channel_blocker	cholesterol_inhibitor	cholinergic_receptor_antagonist	coagulation_factor_inhibitor	corticosteroid_agonist	cyclooxygenase_inhibitor	cytochrome_p450_inhibitor	dihydrofolate_reductase_inhibitor	dipeptidyl_peptidase_inhibitor	diuretic	dna_alkylating_agent	dna_inhibitor	dopamine_receptor_agonist	dopamine_receptor_antagonist	egfr_inhibitor	elastase_inhibitor	erbb2_inhibitor	estrogen_receptor_agonist	estrogen_receptor_antagonist	faah_inhibitor	farnesyltransferase_inhibitor	fatty_acid_receptor_agonist	fgfr_inhibitor	flt3_inhibitor	focal_adhesion_kinase_inhibitor	free_radical_scavenger	fungal_squalene_epoxidase_inhibitor	gaba_receptor_agonist	gaba_receptor_antagonist	gamma_secretase_inhibitor	glucocorticoid_receptor_agonist	glutamate_inhibitor	glutamate_receptor_agonist	glutamate_receptor_antagonist	gonadotropin_receptor_agonist	gsk_inhibitor	hcv_inhibitor	hdac_inhibitor	histamine_receptor_agonist	histamine_receptor_antagonist	histone_lysine_demethylase_inhibitor	histone_lysine_methyltransferase_inhibitor	hiv_inhibitor	hmgcr_inhibitor	hsp_inhibitor	igf-1_inhibitor	ikk_inhibitor	imidazoline_receptor_agonist	immunosuppressant	insulin_secretagogue	insulin_sensitizer	integrin_inhibitor	jak_inhibitor	kit_inhibitor	laxative	leukotriene_inhibitor	leukotriene_receptor_antagonist	lipase_inhibitor	lipoxygenase_inhibitor	lxr_agonist	mdm_inhibitor	mek_inhibitor	membrane_integrity_inhibitor	mineralocorticoid_receptor_antagonist	monoacylglycerol_lipase_inhibitor	monoamine_oxidase_inhibitor	monopolar_spindle_1_kinase_inhibitor	mtor_inhibitor	mucolytic_agent	neuropeptide_receptor_antagonist	nfkb_inhibitor	nicotinic_receptor_agonist	nitric_oxide_donor	nitric_oxide_production_inhibitor	nitric_oxide_synthase_inhibitor	norepinephrine_reuptake_inhibitor	nrf2_activator	opioid_receptor_agonist	opioid_receptor_antagonist	orexin_receptor_antagonist	p38_mapk_inhibitor	p-glycoprotein_inhibitor	parp_inhibitor	pdgfr_inhibitor	pdk_inhibitor	phosphodiesterase_inhibitor	phospholipase_inhibitor	pi3k_inhibitor	pkc_inhibitor	potassium_channel_activator	potassium_channel_antagonist	ppar_receptor_agonist	ppar_receptor_antagonist	progesterone_receptor_agonist	progesterone_receptor_antagonist	prostaglandin_inhibitor	prostanoid_receptor_antagonist	proteasome_inhibitor	protein_kinase_inhibitor	protein_phosphatase_inhibitor	protein_synthesis_inhibitor	protein_tyrosine_kinase_inhibitor	radiopaque_medium	raf_inhibitor	ras_gtpase_inhibitor	retinoid_receptor_agonist	retinoid_receptor_antagonist	rho_associated_kinase_inhibitor	ribonucleoside_reductase_inhibitor	rna_polymerase_inhibitor	serotonin_receptor_agonist	serotonin_receptor_antagonist	serotonin_reuptake_inhibitor	sigma_receptor_agonist	sigma_receptor_antagonist	smoothened_receptor_antagonist	sodium_channel_inhibitor	sphingosine_receptor_agonist	src_inhibitor	steroid	syk_inhibitor	tachykinin_antagonist	tgf-beta_receptor_inhibitor	thrombin_inhibitor	thymidylate_synthase_inhibitor	tlr_agonist	tlr_antagonist	tnf_inhibitor	topoisomerase_inhibitor	transient_receptor_potential_channel_antagonist	tropomyosin_receptor_kinase_inhibitor	trpv_agonist	trpv_antagonist	tubulin_inhibitor	tyrosine_kinase_inhibitor	ubiquitin_specific_protease_inhibitor	vegfr_inhibitor	vitamin_b	vitamin_d_receptor_agonist	wnt_inhibitor
3398	id_248e8ffb9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
14218	id_9908cd099	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
22132	id_ed55cbe23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
21477	id_e638db82b	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3547	id_262b1d2f0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

test_target.head()

	sig_id	5-alpha_reductase_inhibitor	11-beta-hsd1_inhibitor	acat_inhibitor	acetylcholine_receptor_agonist	acetylcholine_receptor_antagonist	acetylcholinesterase_inhibitor	adenosine_receptor_agonist	adenosine_receptor_antagonist	adenylyl_cyclase_activator	adrenergic_receptor_agonist	adrenergic_receptor_antagonist	akt_inhibitor	aldehyde_dehydrogenase_inhibitor	alk_inhibitor	ampk_activator	analgesic	androgen_receptor_agonist	androgen_receptor_antagonist	anesthetic_-_local	angiogenesis_inhibitor	angiotensin_receptor_antagonist	anti-inflammatory	antiarrhythmic	antibiotic	anticonvulsant	antifungal	antihistamine	antimalarial	antioxidant	antiprotozoal	antiviral	apoptosis_stimulant	aromatase_inhibitor	atm_kinase_inhibitor	atp-sensitive_potassium_channel_antagonist	atp_synthase_inhibitor	atpase_inhibitor	atr_kinase_inhibitor	aurora_kinase_inhibitor	autotaxin_inhibitor	bacterial_30s_ribosomal_subunit_inhibitor	bacterial_50s_ribosomal_subunit_inhibitor	bacterial_antifolate	bacterial_cell_wall_synthesis_inhibitor	bacterial_dna_gyrase_inhibitor	bacterial_dna_inhibitor	bacterial_membrane_integrity_inhibitor	bcl_inhibitor	bcr-abl_inhibitor	benzodiazepine_receptor_agonist	beta_amyloid_inhibitor	bromodomain_inhibitor	btk_inhibitor	calcineurin_inhibitor	calcium_channel_blocker	cannabinoid_receptor_agonist	cannabinoid_receptor_antagonist	carbonic_anhydrase_inhibitor	casein_kinase_inhibitor	caspase_activator	catechol_o_methyltransferase_inhibitor	cc_chemokine_receptor_antagonist	cck_receptor_antagonist	cdk_inhibitor	chelating_agent	chk_inhibitor	chloride_channel_blocker	cholesterol_inhibitor	cholinergic_receptor_antagonist	coagulation_factor_inhibitor	corticosteroid_agonist	cyclooxygenase_inhibitor	cytochrome_p450_inhibitor	dihydrofolate_reductase_inhibitor	dipeptidyl_peptidase_inhibitor	diuretic	dna_alkylating_agent	dna_inhibitor	dopamine_receptor_agonist	dopamine_receptor_antagonist	egfr_inhibitor	elastase_inhibitor	erbb2_inhibitor	estrogen_receptor_agonist	estrogen_receptor_antagonist	faah_inhibitor	farnesyltransferase_inhibitor	fatty_acid_receptor_agonist	fgfr_inhibitor	flt3_inhibitor	focal_adhesion_kinase_inhibitor	free_radical_scavenger	fungal_squalene_epoxidase_inhibitor	gaba_receptor_agonist	gaba_receptor_antagonist	gamma_secretase_inhibitor	glucocorticoid_receptor_agonist	glutamate_inhibitor	glutamate_receptor_agonist	glutamate_receptor_antagonist	gonadotropin_receptor_agonist	gsk_inhibitor	hcv_inhibitor	hdac_inhibitor	histamine_receptor_agonist	histamine_receptor_antagonist	histone_lysine_demethylase_inhibitor	histone_lysine_methyltransferase_inhibitor	hiv_inhibitor	hmgcr_inhibitor	hsp_inhibitor	igf-1_inhibitor	ikk_inhibitor	imidazoline_receptor_agonist	immunosuppressant	insulin_secretagogue	insulin_sensitizer	integrin_inhibitor	jak_inhibitor	kit_inhibitor	laxative	leukotriene_inhibitor	leukotriene_receptor_antagonist	lipase_inhibitor	lipoxygenase_inhibitor	lxr_agonist	mdm_inhibitor	mek_inhibitor	membrane_integrity_inhibitor	mineralocorticoid_receptor_antagonist	monoacylglycerol_lipase_inhibitor	monoamine_oxidase_inhibitor	monopolar_spindle_1_kinase_inhibitor	mtor_inhibitor	mucolytic_agent	neuropeptide_receptor_antagonist	nfkb_inhibitor	nicotinic_receptor_agonist	nitric_oxide_donor	nitric_oxide_production_inhibitor	nitric_oxide_synthase_inhibitor	norepinephrine_reuptake_inhibitor	nrf2_activator	opioid_receptor_agonist	opioid_receptor_antagonist	orexin_receptor_antagonist	p38_mapk_inhibitor	p-glycoprotein_inhibitor	parp_inhibitor	pdgfr_inhibitor	pdk_inhibitor	phosphodiesterase_inhibitor	phospholipase_inhibitor	pi3k_inhibitor	pkc_inhibitor	potassium_channel_activator	potassium_channel_antagonist	ppar_receptor_agonist	ppar_receptor_antagonist	progesterone_receptor_agonist	progesterone_receptor_antagonist	prostaglandin_inhibitor	prostanoid_receptor_antagonist	proteasome_inhibitor	protein_kinase_inhibitor	protein_phosphatase_inhibitor	protein_synthesis_inhibitor	protein_tyrosine_kinase_inhibitor	radiopaque_medium	raf_inhibitor	ras_gtpase_inhibitor	retinoid_receptor_agonist	retinoid_receptor_antagonist	rho_associated_kinase_inhibitor	ribonucleoside_reductase_inhibitor	rna_polymerase_inhibitor	serotonin_receptor_agonist	serotonin_receptor_antagonist	serotonin_reuptake_inhibitor	sigma_receptor_agonist	sigma_receptor_antagonist	smoothened_receptor_antagonist	sodium_channel_inhibitor	sphingosine_receptor_agonist	src_inhibitor	steroid	syk_inhibitor	tachykinin_antagonist	tgf-beta_receptor_inhibitor	thrombin_inhibitor	thymidylate_synthase_inhibitor	tlr_agonist	tlr_antagonist	tnf_inhibitor	topoisomerase_inhibitor	transient_receptor_potential_channel_antagonist	tropomyosin_receptor_kinase_inhibitor	trpv_agonist	trpv_antagonist	tubulin_inhibitor	tyrosine_kinase_inhibitor	ubiquitin_specific_protease_inhibitor	vegfr_inhibitor	vitamin_b	vitamin_d_receptor_agonist	wnt_inhibitor
12276	id_843baf550	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
8181	id_57dc82c5a	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4667	id_322b2f678	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
15548	id_a76c49729	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
15206	id_a35ce02ff	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Final goal:

Use the training dataset to develop an algorithm that automatically labels each case in the test set as one or more MoA classes.

This is a multi-label classification problem. (Each class is defined by k of n labels)

Model prediction goal:

For every sig_id you will be predicting the probability that the sample had a positive response for each MOA target.

Assessment of the model:

For N sig_id rows and M MOA targets, the model will be making N×M predictions. Model are assessed by the log loss:

$score = -\frac{1}{M}\sum^{M}_{m=1}{\frac{1}{N}\sum^{N}_i=1}[y_{i,m} log(\hat y_{i,m}) + (1-y_{i,m})log(1-\hat y_{i,m})]$

• $N$ is the number of sig_id observations in the test data ($i=1,…,N$)
• $M$ is the number of scored MoA targets ($m=1,…,M$)
• $\hat y_{i,m}$ is the predicted probability of a positive MoA response for a sig_id (the predicted probability of each class)
• $y_{i,m}$ is the ground truth, 1 for a positive response, 0 otherwise
• $log()$ is the natural (base e) logarithm

Data set:

There are three files in total:

train_features.csv - Features for the training set. Features g- signify gene expression data, and c- signify cell viability data. cp_type indicates samples treated with a compound (cp_vehicle) or with a control perturbation (ctrl_vehicle); control perturbations have no MoAs; cp_time and cp_dose indicate treatment duration (24, 48, 72 hours) and dose (high or low).

train_drug.csv - This file contains an anonymous drug_id for the training set only.

train_targets_scored.csv - The binary MoA targets that are scored.

train_targets_nonscored.csv - Additional (optional) binary MoA responses for the training data. These are not predicted nor scored.

Base line of EDA:

1. Basic data overview
2. Categories visualization
3. Gene and cell features distribution
4. Checking features correlation
5. Targets analysis on the training set
6. Train & Targets correlations: find the most correlated features for every target column.

# review of the training and testing dataset
train_features.head()

	sig_id	cp_type	cp_time	cp_dose	g-0	g-1	g-2	g-3	g-4	g-5	g-6	g-7	g-8	g-9	g-10	g-11	g-12	g-13	g-14	g-15	g-16	g-17	g-18	g-19	g-20	g-21	g-22	g-23	g-24	g-25	g-26	g-27	g-28	g-29	g-30	g-31	g-32	g-33	g-34	g-35	g-36	g-37	g-38	g-39	g-40	g-41	g-42	g-43	g-44	g-45	g-46	g-47	g-48	g-49	g-50	g-51	g-52	g-53	g-54	g-55	g-56	g-57	g-58	g-59	g-60	g-61	g-62	g-63	g-64	g-65	g-66	g-67	g-68	g-69	g-70	g-71	g-72	g-73	g-74	g-75	g-76	g-77	g-78	g-79	g-80	g-81	g-82	g-83	g-84	g-85	g-86	g-87	g-88	g-89	g-90	g-91	g-92	g-93	g-94	g-95	g-96	g-97	g-98	g-99	g-100	g-101	g-102	g-103	g-104	g-105	g-106	g-107	g-108	g-109	g-110	g-111	g-112	g-113	g-114	g-115	g-116	g-117	g-118	g-119	g-120	g-121	g-122	g-123	g-124	g-125	g-126	g-127	g-128	g-129	g-130	g-131	g-132	g-133	g-134	g-135	g-136	g-137	g-138	g-139	g-140	g-141	g-142	g-143	g-144	g-145	g-146	g-147	g-148	g-149	g-150	g-151	g-152	g-153	g-154	g-155	g-156	g-157	g-158	g-159	g-160	g-161	g-162	g-163	g-164	g-165	g-166	g-167	g-168	g-169	g-170	g-171	g-172	g-173	g-174	g-175	g-176	g-177	g-178	g-179	g-180	g-181	g-182	g-183	g-184	g-185	g-186	g-187	g-188	g-189	g-190	g-191	g-192	g-193	g-194	g-195	g-196	g-197	g-198	g-199	g-200	g-201	g-202	g-203	g-204	g-205	g-206	g-207	g-208	g-209	g-210	g-211	g-212	g-213	g-214	g-215	g-216	g-217	g-218	g-219	g-220	g-221	g-222	g-223	g-224	g-225	g-226	g-227	g-228	g-229	g-230	g-231	g-232	g-233	g-234	g-235	g-236	g-237	g-238	g-239	g-240	g-241	g-242	g-243	g-244	g-245	g-246	g-247	g-248	g-249	g-250	g-251	g-252	g-253	g-254	g-255	g-256	g-257	g-258	g-259	g-260	g-261	g-262	g-263	g-264	g-265	g-266	g-267	g-268	g-269	g-270	g-271	g-272	g-273	g-274	g-275	g-276	g-277	g-278	g-279	g-280	g-281	g-282	g-283	g-284	g-285	g-286	g-287	g-288	g-289	g-290	g-291	g-292	g-293	g-294	g-295	g-296	g-297	g-298	g-299	g-300	g-301	g-302	g-303	g-304	g-305	g-306	g-307	g-308	g-309	g-310	g-311	g-312	g-313	g-314	g-315	g-316	g-317	g-318	g-319	g-320	g-321	g-322	g-323	g-324	g-325	g-326	g-327	g-328	g-329	g-330	g-331	g-332	g-333	g-334	g-335	g-336	g-337	g-338	g-339	g-340	g-341	g-342	g-343	g-344	g-345	g-346	g-347	g-348	g-349	g-350	g-351	g-352	g-353	g-354	g-355	g-356	g-357	g-358	g-359	g-360	g-361	g-362	g-363	g-364	g-365	g-366	g-367	g-368	g-369	g-370	g-371	g-372	g-373	g-374	g-375	g-376	g-377	g-378	g-379	g-380	g-381	g-382	g-383	g-384	g-385	g-386	g-387	g-388	g-389	g-390	g-391	g-392	g-393	g-394	g-395	g-396	g-397	g-398	g-399	g-400	g-401	g-402	g-403	g-404	g-405	g-406	g-407	g-408	g-409	g-410	g-411	g-412	g-413	g-414	g-415	g-416	g-417	g-418	g-419	g-420	g-421	g-422	g-423	g-424	g-425	g-426	g-427	g-428	g-429	g-430	g-431	g-432	g-433	g-434	g-435	g-436	g-437	g-438	g-439	g-440	g-441	g-442	g-443	g-444	g-445	g-446	g-447	g-448	g-449	g-450	g-451	g-452	g-453	g-454	g-455	g-456	g-457	g-458	g-459	g-460	g-461	g-462	g-463	g-464	g-465	g-466	g-467	g-468	g-469	g-470	g-471	g-472	g-473	g-474	g-475	g-476	g-477	g-478	g-479	g-480	g-481	g-482	g-483	g-484	g-485	g-486	g-487	g-488	g-489	g-490	g-491	g-492	g-493	g-494	g-495	g-496	g-497	g-498	g-499	g-500	g-501	g-502	g-503	g-504	g-505	g-506	g-507	g-508	g-509	g-510	g-511	g-512	g-513	g-514	g-515	g-516	g-517	g-518	g-519	g-520	g-521	g-522	g-523	g-524	g-525	g-526	g-527	g-528	g-529	g-530	g-531	g-532	g-533	g-534	g-535	g-536	g-537	g-538	g-539	g-540	g-541	g-542	g-543	g-544	g-545	g-546	g-547	g-548	g-549	g-550	g-551	g-552	g-553	g-554	g-555	g-556	g-557	g-558	g-559	g-560	g-561	g-562	g-563	g-564	g-565	g-566	g-567	g-568	g-569	g-570	g-571	g-572	g-573	g-574	g-575	g-576	g-577	g-578	g-579	g-580	g-581	g-582	g-583	g-584	g-585	g-586	g-587	g-588	g-589	g-590	g-591	g-592	g-593	g-594	g-595	g-596	g-597	g-598	g-599	g-600	g-601	g-602	g-603	g-604	g-605	g-606	g-607	g-608	g-609	g-610	g-611	g-612	g-613	g-614	g-615	g-616	g-617	g-618	g-619	g-620	g-621	g-622	g-623	g-624	g-625	g-626	g-627	g-628	g-629	g-630	g-631	g-632	g-633	g-634	g-635	g-636	g-637	g-638	g-639	g-640	g-641	g-642	g-643	g-644	g-645	g-646	g-647	g-648	g-649	g-650	g-651	g-652	g-653	g-654	g-655	g-656	g-657	g-658	g-659	g-660	g-661	g-662	g-663	g-664	g-665	g-666	g-667	g-668	g-669	g-670	g-671	g-672	g-673	g-674	g-675	g-676	g-677	g-678	g-679	g-680	g-681	g-682	g-683	g-684	g-685	g-686	g-687	g-688	g-689	g-690	g-691	g-692	g-693	g-694	g-695	g-696	g-697	g-698	g-699	g-700	g-701	g-702	g-703	g-704	g-705	g-706	g-707	g-708	g-709	g-710	g-711	g-712	g-713	g-714	g-715	g-716	g-717	g-718	g-719	g-720	g-721	g-722	g-723	g-724	g-725	g-726	g-727	g-728	g-729	g-730	g-731	g-732	g-733	g-734	g-735	g-736	g-737	g-738	g-739	g-740	g-741	g-742	g-743	g-744	g-745	g-746	g-747	g-748	g-749	g-750	g-751	g-752	g-753	g-754	g-755	g-756	g-757	g-758	g-759	g-760	g-761	g-762	g-763	g-764	g-765	g-766	g-767	g-768	g-769	g-770	g-771	c-0	c-1	c-2	c-3	c-4	c-5	c-6	c-7	c-8	c-9	c-10	c-11	c-12	c-13	c-14	c-15	c-16	c-17	c-18	c-19	c-20	c-21	c-22	c-23	c-24	c-25	c-26	c-27	c-28	c-29	c-30	c-31	c-32	c-33	c-34	c-35	c-36	c-37	c-38	c-39	c-40	c-41	c-42	c-43	c-44	c-45	c-46	c-47	c-48	c-49	c-50	c-51	c-52	c-53	c-54	c-55	c-56	c-57	c-58	c-59	c-60	c-61	c-62	c-63	c-64	c-65	c-66	c-67	c-68	c-69	c-70	c-71	c-72	c-73	c-74	c-75	c-76	c-77	c-78	c-79	c-80	c-81	c-82	c-83	c-84	c-85	c-86	c-87	c-88	c-89	c-90	c-91	c-92	c-93	c-94	c-95	c-96	c-97	c-98	c-99
3398	id_248e8ffb9	trt_cp	72	D1	0.4720	0.1419	1.2340	-0.0057	-0.0911	0.2410	-0.8717	0.0135	0.3493	0.6811	0.7140	0.1887	0.2725	0.1123	0.6453	0.4442	-0.2373	0.3990	-0.0692	-0.3642	-0.4664	0.6897	0.0481	-0.2889	0.0993	-0.2063	0.3582	0.6302	0.3224	0.5446	0.9693	-0.0580	0.7535	-0.5612	0.3088	-0.7069	-0.2801	0.3787	0.1803	-0.0121	-0.2536	-1.0610	-0.8663	-0.1441	1.5680	-0.3688	-0.5008	0.4206	0.6536	0.2332	0.4886	0.2253	0.7554	0.2403	-0.2562	0.6948	-3.7700	0.6281	0.2524	-0.0277	0.6064	0.2324	0.7034	0.7585	0.3465	0.2081	-0.1364	0.7235	0.1890	-0.2038	0.4679	0.1603	0.8130	-0.3708	-0.8269	0.5570	-0.1521	-1.1720	-0.7718	0.4962	-0.2962	0.4041	0.6985	-0.4771	0.3628	-0.7007	-0.0834	1.3550	0.5939	-0.4810	0.8269	-0.9773	0.2720	0.0150	0.3308	0.0462	0.9807	-0.4678	-0.1629	0.5652	-0.7909	-0.3317	-0.3041	-0.7765	0.3929	0.2806	-0.2997	0.0297	0.2516	0.7912	-0.0277	-0.3650	0.7923	1.1710	0.5946	-0.0003	-0.2305	1.1000	0.3190	0.3078	-0.4188	1.0140	-0.0580	0.1488	-0.0155	-0.9067	-0.9141	0.1225	0.4488	0.1645	-0.4259	-0.0217	0.0000	-0.5446	-0.5133	-0.5638	0.3978	0.0234	0.3434	-0.4541	0.3246	-0.3262	-0.1457	0.3334	-0.3503	1.3790	-0.9252	-0.7129	0.3000	0.3673	0.3394	0.3270	-0.3314	-0.1273	0.6144	-0.2685	0.3517	0.1856	-0.5720	-0.7771	0.7167	-0.4932	-0.2680	-0.1384	0.1748	-0.0992	-0.4438	0.9316	-1.0990	-0.0561	-0.3303	0.2060	0.8681	0.0648	0.2324	-0.1855	-0.1547	-0.8109	-0.3122	0.6035	-0.2672	-0.2714	-0.1384	0.1326	0.5382	0.4850	0.1551	0.6940	-0.3140	0.3677	-0.1704	0.1455	0.8168	-0.3629	0.4730	0.0654	0.5081	0.1983	0.2038	0.2330	-0.2455	-0.0965	0.1820	0.7775	0.1905	-0.4416	0.4520	-0.0796	-0.7482	0.3770	-0.0966	1.0770	0.1364	0.0752	0.6315	-0.5565	-0.4284	1.0580	0.7432	-0.1531	-0.1441	-0.0251	0.6279	0.4780	-0.9244	-0.5389	-0.5898	-0.3708	1.1620	0.4353	-0.2804	-0.6569	-0.0959	0.4874	0.1850	0.1239	-0.2626	-0.9216	0.0000	0.0878	0.3332	0.6927	0.1248	-0.8233	0.2138	-0.1171	0.0000	0.6072	-0.4298	-0.1231	0.3628	-0.7907	-1.1480	-0.5342	0.0204	0.3678	-0.5601	-0.4925	0.6046	-0.2246	-0.6891	-0.0411	0.3372	0.5045	-0.6819	-0.4131	-0.2208	-0.2442	-0.1297	0.3553	0.0350	-0.5359	-0.4371	1.4220	-0.1025	-0.2839	-0.0789	0.3810	-0.1183	-0.2305	0.0925	0.1045	1.2880	-0.9276	0.5041	0.6319	2.0210	0.2799	0.1538	-0.0418	-0.0531	0.3988	0.3450	0.2457	0.8063	-0.3637	0.9918	0.3740	-0.4128	0.2577	-0.4638	0.2729	0.1989	0.1794	0.0234	-0.1996	0.1019	0.0000	-0.6502	-0.1470	0.2352	-0.8686	0.4998	-1.2050	-0.0876	-0.4466	-0.0428	-0.1069	0.8184	-0.4084	-0.2310	-0.2391	0.7287	-0.8955	0.4004	-0.2874	-0.3715	0.4493	-0.5716	-0.2706	-0.2684	-0.2606	0.9036	-0.3325	0.2725	0.1777	-0.1937	-0.3740	0.1609	0.3214	-0.3202	-0.2812	0.2917	0.1531	-0.4284	0.2216	0.4863	-0.5310	-0.0576	-0.0067	0.3272	0.3602	-0.0155	0.7498	-0.0940	0.1206	-0.0700	0.5596	-0.3830	-1.0410	0.8573	0.0958	1.1890	0.3467	0.8388	0.0527	-0.1567	-0.1479	0.3493	-0.3126	0.0000	-0.6815	-0.3949	0.7843	-0.6365	1.1140	-3.6580	-0.7200	0.5073	-0.9623	1.7160	0.3859	-0.1599	2.5370	-0.0561	0.0338	-0.1958	0.2060	-0.1979	0.3683	-0.1619	1.5990	-0.4578	-0.0051	0.2293	0.5936	-0.6730	-0.3368	0.0179	1.2660	0.0536	-1.1770	-1.3290	-0.8482	-0.1409	-0.8978	1.0060	-0.0458	-0.1705	-0.0371	-0.2792	-0.6878	0.5290	0.6991	0.5070	0.4534	-0.2584	-0.7230	-0.4337	-0.2228	-0.2769	-0.1244	0.0335	0.2648	0.4784	0.3238	-0.4792	-0.1447	-0.1041	-0.2944	0.8827	0.4032	0.3531	0.4480	0.9130	-0.3362	0.6729	0.2654	0.2794	0.5076	0.0101	-0.0534	0.6752	-0.3135	-0.0660	-0.2729	0.7341	-0.1962	-0.0550	0.6075	-0.2591	0.0255	1.2650	0.1272	-0.9554	-0.5107	0.7555	0.0863	-0.3456	-0.2909	-0.3942	0.9318	0.0127	0.1229	-0.0962	-0.3726	0.1811	0.1378	-0.3095	0.1290	0.0600	0.2847	-0.5960	-0.2738	-0.3382	-0.0431	0.2760	-0.2080	-0.2988	1.1700	0.3225	0.3487	-0.0714	-1.8100	-0.6262	0.5608	-0.0175	-0.5735	0.3384	1.1790	0.0831	0.1594	-0.7274	0.1530	-0.1838	0.0965	0.1191	0.1579	-0.3596	0.2200	0.3052	-0.1337	-1.0980	0.7261	-0.3652	-0.0022	0.2797	-1.0810	0.1717	0.2698	0.3204	-0.3676	0.0702	0.8797	-1.2850	1.5310	-0.0934	-0.6107	-0.3314	-0.1176	-0.0377	-0.3253	0.6661	0.4494	0.2485	0.6646	-0.2620	0.1990	0.0931	0.4038	-0.1618	-0.7225	-0.5920	-0.7095	0.6049	-0.3099	0.2165	-0.9167	0.0252	0.2902	0.1310	-0.1042	0.8054	-0.6336	1.8290	0.5814	0.1781	-0.5513	-0.1049	-0.4270	-0.1413	-0.2229	0.6557	-0.0869	0.3191	0.4035	3.1910	0.1514	-0.5138	-0.6161	-0.2792	0.5358	0.7187	0.4101	0.0989	-0.2346	-0.9556	-0.0360	-1.2040	0.2709	-1.8180	0.6641	0.4067	0.7871	0.5939	-0.6870	-0.1356	0.9689	-0.2781	-0.0770	-0.3558	-0.3575	-0.0541	0.0500	0.2044	0.3447	1.0300	0.4234	0.1513	-0.3747	-0.4731	-0.2006	-0.6104	-0.1318	-0.0714	0.2860	0.0867	0.6102	1.2410	-0.6326	-0.1383	0.3584	-0.3256	-0.0097	0.6514	0.2012	0.0750	0.2588	-0.6174	-0.5586	-1.4150	0.4384	-0.0500	-0.1578	1.0950	0.9078	-0.5739	0.5347	-0.0364	-0.2984	-0.5265	-0.4723	-0.5071	-0.2709	0.2312	-0.8223	-0.0222	0.2431	-1.6970	-0.0301	-0.0391	0.0899	0.4365	-1.0030	0.1199	0.0764	0.5810	-0.0936	-0.0193	0.4015	0.1238	0.6536	-0.0708	-0.0254	-0.1027	-0.2643	-0.6852	-0.3773	-0.3407	-0.4757	-0.6766	-0.2443	0.7211	0.1495	0.3645	0.8615	-0.2058	0.4070	0.0678	-0.7170	0.3115	0.9636	-0.1343	0.7204	-0.0430	-0.6118	1.2150	0.8809	-0.2761	0.9877	0.2349	-0.0739	-0.2244	0.6989	-0.6270	0.8359	-0.2456	0.2866	0.6802	-0.3336	0.3032	0.7620	-0.5580	-0.1549	-0.0908	0.1872	-0.0265	0.0587	-0.2337	0.4516	-0.1282	0.3720	-1.6540	-0.8153	-1.0370	-0.3118	-0.2924	-0.1905	-0.4933	-0.6302	-0.4474	0.1821	0.4436	0.5951	-0.5001	-0.2725	0.1200	0.0000	0.3350	0.3423	-0.2701	0.1987	-0.9478	-0.2927	0.5217	0.8529	-0.4727	-0.1586	-0.0883	-0.1114	0.1291	-0.2954	-0.7224	-0.1538	-0.0702	-0.6000	-0.2698	0.0247	-0.5051	-0.1636	1.3340	0.2358	-0.7406	0.7409	-0.0634	0.2987	-0.3532	1.0180	0.4808	0.4440	0.1062	0.1704	0.3964	0.0196	-0.6464	-0.7026	-3.0890	-0.2540	-1.0710	0.9238	-0.3652	0.2297	1.4520	0.7447	-0.1815	0.5649	0.1817	-0.2937	-0.1784	0.0215	-0.4211	0.0837	-0.0066	-0.4748	0.0955	0.0575	1.6360	-0.2013	-0.6367	0.6093	0.6482	-0.2409	-0.0884	0.1221	-0.3541	-0.6384	-0.1507	0.0261	0.0585	0.1678	0.2574	-0.1458	0.2744	-0.7857	0.1500	0.0666	0.1131	0.3086	0.0472	0.6668	0.2298	0.1818	0.6436	-0.1346	0.5656	-0.5927	-0.8505	0.0874	0.5917	0.2262	0.3038	0.1036	0.6890	-0.5311	-0.4023	-0.1433	0.4986	0.0919	-0.1387	-0.2938	-0.3111	-0.7643	-0.1310	-0.0564	0.2086	-0.3739	-0.6299	0.1938	0.6858	0.8561	-0.0859	-0.4144	0.3192	0.1021	-0.1341	-0.4381	0.1656	0.5185	-0.0485	0.2558	-1.5410	0.5239	-0.1015	-0.9810	0.5986	0.4147	0.2444	-0.3656	-0.0717	1.1210	-0.2044	0.0898	0.5739	0.3435	0.0419	-0.1146	0.6618	0.2079	-0.3788	0.6100	-0.0361	0.5885	-0.3301	0.2882	-0.2505	-0.7039	-0.7816	-0.1358	0.0677	0.2065	-0.7324	0.6226	-0.2500	0.1578	0.5265	0.0871	-0.4861	0.3178	0.4900	0.7219	-0.1344	-0.2595
14218	id_9908cd099	trt_cp	72	D2	-0.3158	0.8177	0.0701	-0.2758	0.0029	-4.6850	-0.2011	0.6514	-0.0514	0.6010	-0.2656	0.0889	4.9550	-0.6653	-0.9908	0.2441	1.7810	-0.8020	0.2576	0.1824	-0.2335	-0.2463	-0.4961	-0.6468	0.0619	0.2470	1.3830	0.0142	0.2944	0.1275	0.1806	-0.3934	-0.1976	-0.0121	-0.1390	-0.2695	-0.2295	0.1230	-0.8702	-0.2266	-0.7892	-0.5480	0.3901	-0.5864	-1.1330	-0.2038	-0.5362	0.0155	-0.2316	0.8293	-0.7612	-0.0366	0.2351	-0.3615	-0.7040	0.7009	0.5207	0.5047	1.4000	-0.9054	0.2734	-0.0582	-0.6289	0.1997	0.7021	-0.9662	0.8772	0.9417	-0.2700	0.6893	0.3877	-1.0520	0.2094	0.2781	-0.7220	0.1770	1.2240	0.2311	-0.6787	0.1079	-0.1637	0.6391	0.6511	-1.0140	0.0985	-0.6080	-1.1030	-0.8918	-0.6229	1.9080	0.4466	-0.6023	-0.3299	-0.2116	0.5102	-1.0750	0.2901	0.9844	0.3004	-0.6873	-1.0040	1.9640	0.2191	-0.9509	0.5362	-0.3151	0.3324	-0.0230	-1.9920	1.5130	-0.1247	-0.2190	0.5342	-3.9510	0.2538	-1.4600	0.3535	0.1981	0.2538	1.0640	0.5518	0.0589	-0.8049	-0.8765	-1.0010	0.1688	-0.1187	0.1224	-0.2128	2.8790	0.6348	1.6180	-0.4803	0.0353	-0.3382	0.1012	-0.3473	0.7512	-0.0045	0.1966	-0.1731	2.1200	0.2004	-0.7107	-0.8740	-1.0910	0.0394	-0.1132	1.0350	-0.2164	0.5905	-0.0713	0.4734	-0.4938	0.1229	-0.9519	-0.3587	0.6475	-0.5459	-0.5418	1.1990	-0.4283	0.5734	-0.3781	0.9622	0.1753	-0.4398	0.6718	-0.9918	0.7547	-0.0403	0.7993	0.7224	0.0379	-0.3151	0.7893	0.0517	0.5382	0.3372	0.4660	0.3810	0.2570	1.0480	1.3150	0.0985	-0.4429	-0.0442	0.6794	0.2685	0.0116	-2.0220	-2.0150	-1.5220	-0.0510	0.7635	1.4360	0.2766	-0.1605	0.1817	0.0993	-0.5477	0.1060	-0.1993	-0.3918	-0.1300	-0.2964	-0.0902	0.9211	2.5430	1.1030	0.6876	0.2307	1.2630	-0.2141	0.6012	0.7023	0.1271	0.8230	5.4980	0.7925	-0.3979	-0.3650	0.3267	0.5453	1.1360	-0.8456	-0.3289	-3.0120	0.9161	-0.2899	-0.0436	0.1100	-0.3117	-1.3830	0.5012	0.1727	0.0198	1.3420	-0.7555	0.9145	-0.3285	-1.0420	0.3062	-0.4130	0.1555	0.3607	-0.6664	0.2289	0.3407	-0.5737	0.4694	-0.4771	-0.4144	1.1150	0.3066	0.1883	-0.1420	-0.2452	0.5853	-0.8226	-1.4680	-0.7152	-0.6070	-0.0969	-0.8695	-0.2361	0.4421	-0.7175	-0.5653	-0.4024	0.3365	0.4948	-0.3656	-0.9827	0.3357	1.0750	1.0510	0.4455	0.4983	0.7844	2.5140	0.6538	0.7731	-0.3131	1.2440	1.1630	-0.2348	0.0710	0.2975	-0.0362	0.4015	1.6550	-0.1477	0.1712	-0.2937	-0.1059	1.0770	0.0935	0.3957	-0.6524	0.5210	-0.3351	0.0239	0.4158	1.2380	0.0398	-0.7002	-0.0573	0.8667	-0.1055	-0.8131	-0.8674	0.7214	1.8010	0.0921	-0.6346	-0.1526	-0.3578	1.1400	-0.0780	-0.6852	0.0228	-0.2213	1.9830	0.7426	-3.6980	-0.3643	0.2982	-0.6424	0.7607	0.0118	0.3109	-0.0718	-0.3983	-0.7420	-0.1701	0.4539	0.2550	1.5720	0.4861	-0.3803	-0.0379	0.3686	-0.2639	0.1885	0.0733	-1.0360	-1.5930	-0.1448	0.5451	-1.8140	-0.0718	0.9023	0.1989	-0.6886	1.0790	-0.9276	-0.1827	-0.7424	-0.0818	0.0964	0.2000	0.2942	-0.4947	-1.1060	-0.3148	-0.2288	-0.8482	-0.3159	0.9295	-1.5850	0.2285	-0.2413	0.0441	-0.6938	3.6840	-1.2620	-0.4023	-0.4963	-0.3656	1.6320	0.3554	0.0940	0.3972	1.3120	-0.1633	0.0851	-0.2818	-0.9318	0.7550	0.0442	0.5106	-0.2088	-1.1120	-1.9430	1.1020	0.3602	-0.5858	-0.4857	-0.1491	0.1407	1.4270	0.4453	-0.3240	0.0617	-0.0015	-0.7510	-0.0593	0.3539	-1.0470	0.5360	0.5925	0.0868	2.2260	0.7367	0.5078	-0.7858	-0.3469	-1.1440	-0.4288	-0.5824	-0.2854	0.7254	0.1490	0.6745	1.2880	0.5358	-0.1655	-1.4200	0.6852	-1.8910	-1.0510	-0.2863	-0.7643	-0.0203	-0.3258	0.1432	-0.3763	-1.0960	-0.2959	0.3628	0.3233	0.2720	-0.3142	-0.7829	0.7801	0.1501	1.2090	0.4747	-0.3221	-0.8381	0.7478	0.7225	0.8964	-0.5106	0.3568	0.2837	0.7473	-0.7458	1.2530	-0.0635	0.1043	0.4686	-0.7297	-0.0949	0.9272	0.2559	-0.3490	-0.1341	1.8820	1.0890	-0.3542	0.6380	-0.5203	1.2030	1.0430	0.3702	-1.0780	-0.1737	0.2199	-0.1711	-0.1420	0.0398	-1.7070	-0.1914	0.5984	0.3368	0.9397	-0.3352	-0.2160	0.4201	-0.0995	0.5173	-0.3251	-0.4944	0.7211	-0.6502	-0.6367	-0.0799	0.3086	1.2480	0.1942	-0.9162	1.5360	-0.6308	2.3490	-0.5716	0.0160	-0.0285	0.2484	0.7645	0.7021	0.3916	-0.3249	-0.5323	-0.4073	0.6390	1.0640	0.2269	-0.6528	-2.2590	-0.3415	-0.2149	0.4723	-0.2979	0.0832	-0.3909	0.0276	0.1916	0.8005	-0.1181	-0.0164	-0.0230	4.8560	0.4150	0.3293	0.2988	-0.9150	0.3386	0.0444	0.7254	-0.0228	-0.8720	0.1781	0.4374	-0.1558	1.1500	-0.6723	2.8920	-0.1451	-0.8529	-0.2291	0.1427	0.1211	0.7858	-0.7944	-0.0806	-0.3029	0.4873	-0.5273	0.0898	-0.5106	0.1822	-0.0677	-0.5885	1.0690	-0.3062	0.4919	0.1010	-0.4488	-0.5467	0.0561	0.0475	0.6968	-0.2955	-0.0849	-0.8662	-0.7502	-0.6459	-2.6280	-0.2796	0.1597	2.1470	-0.2401	-0.0670	-1.4260	0.4691	0.3094	0.8269	0.8125	-0.0887	0.9985	-0.1176	0.1264	-0.0490	0.6212	1.1230	0.3909	-1.0580	-0.2862	0.5988	0.0668	0.3094	-0.2914	-2.0900	0.1282	-0.0968	0.6503	5.5050	1.1220	-0.1613	0.5419	-0.0784	0.0486	-0.5166	0.1203	-0.3372	-3.1080	0.1093	0.1834	-1.3370	-0.2391	-0.4849	0.2340	0.5049	-0.3854	-1.9250	1.2100	-0.3575	-0.4515	0.3074	0.2946	0.4665	-0.6077	-1.1480	-0.2226	0.7326	-0.3423	0.4613	0.7110	-0.4247	-0.1571	-1.5060	-1.1980	-1.4760	-0.1314	0.4309	0.2362	-0.1499	0.2633	-1.6330	0.2861	0.3497	1.1260	-5.4940	-0.2759	0.4089	-0.7514	0.2700	-0.3285	-2.2890	0.2757	-0.0779	-0.4169	0.5123	1.3990	0.3678	0.2549	0.2639	0.6603	-0.7154	-0.0673	0.7372	1.0960	-0.6308	0.2263	0.0549	0.3266	-0.7689	0.6889	1.1940	1.0730	0.2694	-0.5203	-1.0440	0.1787	-0.2431	0.3431	-0.3679	-0.1707	-1.0960	0.7613	1.8580	2.6220	0.8863	0.3001	0.7732	-0.1881	-0.4381	0.9127	0.0879	-0.1189	0.8208	1.6010	-2.1420	-5.7430	0.7207	0.0000	0.1685	-0.0945	0.1226	0.2947	-0.2385	0.3360	1.1120	-0.4881	0.6867	-0.0858	0.1227	0.2307	0.3166	1.3980	0.4177	-0.0964	0.6046	2.5260	-0.1379	-0.2060	0.0627	0.5889	-2.0050	0.2044	0.4755	0.3403	0.8790	-0.6391	-0.3178	-3.7360	0.1265	0.0775	0.0491	-1.1680	-0.0705	0.0766	-0.8104	0.8978	0.1777	1.1480	-0.3743	0.8658	-0.0496	0.3924	-3.9720	0.1861	-0.0580	1.3880	0.1304	-0.1157	0.8607	0.7422	1.8470	0.4953	0.4663	0.1777	-0.9287	0.0607	-1.0210	0.1989	-1.4800	0.6980	0.1953	0.0385	-0.0186	0.0144	0.1896	1.4470	0.7838	0.9493	0.5701	0.8357	0.8411	0.1270	0.1620	0.7407	-0.1244	0.4433	0.9726	0.5832	-0.1097	0.6306	-0.0499	0.3914	-0.3555	-0.1004	0.3809	0.5498	0.6711	-0.4162	0.1251	0.1267	0.5785	0.9139	0.3304	0.3477	1.4080	0.2076	-0.0984	0.2691	1.0540	0.4908	-0.2448	1.2890	0.3897	0.9788	1.0860	0.6373	0.8353	-0.3099	0.6204	1.0460	0.5983	0.2524	0.3938	-0.0031	0.7459	0.5655	0.9529	-0.2454	0.8231	-0.2553	-0.3234	-0.3209	0.9830	0.7141	0.2842	-0.1720	0.0173	0.6851	0.0005	0.4363	0.6554	1.2240	-0.3632	0.7649	0.6299	0.5001	0.5753	-0.5215	0.9599	1.1310	0.6852	0.8119	0.2183	1.0760	0.3189	0.6436	0.8135	-0.3913	0.2916	1.0340	-0.3059	0.5679	-0.3740	0.5145	0.1660	0.6712	0.2404	-0.0469	-0.1427	0.0183	1.0090	0.6120
22132	id_ed55cbe23	trt_cp	48	D2	0.2585	0.7555	-0.1093	-0.8668	-0.3000	-3.4150	-1.2360	-0.1521	0.4374	0.0859	0.4024	-0.0358	0.4228	0.1517	0.5498	-0.6309	0.0423	0.6549	-0.6684	-0.8591	-0.4563	0.1219	0.6293	0.5726	0.0124	0.0401	0.2420	0.4949	0.0640	-0.4258	0.4573	-0.4415	0.2127	-0.4656	-0.1479	1.0250	0.1411	0.7308	-0.0852	-0.2401	-0.9634	0.0432	0.2925	0.4856	0.5362	-1.8810	0.3664	0.3909	0.9402	0.7487	-0.3881	-0.2934	-0.2580	-0.0267	0.5339	-0.0394	0.1146	0.3748	0.3119	0.6665	-0.3329	-0.2465	1.0740	-0.5809	-0.2034	0.2284	0.0638	0.0163	0.1397	-0.4501	-0.1949	-0.2616	-0.2361	0.6217	-0.3899	0.2278	-0.7900	0.7579	-0.9938	1.3800	0.6099	-0.4344	1.0730	-1.0230	0.0418	0.0868	0.0756	-0.1717	-0.5990	-0.2667	-0.7780	-0.7330	0.2248	-0.9312	0.7588	-1.0200	1.0690	-0.0611	0.7982	0.6556	-0.3036	-0.0886	0.0148	-0.8472	0.0857	0.3478	0.6121	0.5788	0.8076	0.5354	0.2479	0.0000	0.3760	0.1752	0.7325	0.6551	-0.6711	1.0930	0.4490	0.4522	-0.6832	0.9727	-0.1024	0.0500	-0.5407	0.3980	-0.4163	-0.3338	0.2370	2.2720	-1.4020	0.5797	-0.6455	0.0263	-0.5971	-0.4132	-0.5292	0.2589	0.9895	-1.1130	0.8159	0.8086	-1.0870	0.7995	0.2950	-0.6303	0.1637	-0.7050	-0.8077	0.2478	0.5148	0.6022	-0.3356	0.0000	2.2600	0.2523	0.8760	0.5410	-0.1515	-0.9436	0.2859	2.0180	0.0693	-0.4360	-0.6725	0.2950	-0.6411	0.3339	-1.2620	0.3387	-0.6884	0.2635	-1.8020	0.2046	-0.3636	-0.3961	0.4536	-0.6448	-0.7485	0.6739	-0.7333	-0.5276	-0.1022	-0.2826	0.1787	0.2831	0.6583	0.5651	-0.5131	1.1390	-0.6086	-0.0239	-0.0083	-0.4546	0.6206	0.5910	-0.2151	0.2743	0.4111	1.2520	0.7115	-0.4401	-1.0080	-0.4778	-0.3474	0.4497	-0.0844	-0.9831	-0.4937	0.4297	-0.7901	0.4205	0.2042	-0.2151	0.2482	0.5234	-0.0591	-0.3615	0.1167	-0.6735	1.8670	-1.0570	0.8821	-0.4356	-0.4204	-0.5534	-0.8821	-0.1741	0.1603	-0.1825	0.3444	0.8027	0.7553	1.0950	0.5915	-0.1542	-0.7348	-0.9399	0.3710	0.2028	-0.2192	-1.2020	0.8068	-0.4878	-0.2455	-0.0155	-0.8660	1.1800	0.3646	0.0034	0.1132	-0.7553	-5.3700	-0.6226	-0.3553	0.3731	0.6994	-0.6299	-1.0430	0.3874	-0.0948	-0.3223	0.1949	0.7635	-0.5483	0.0859	0.1947	-0.2665	-0.7382	0.6648	0.7964	0.2423	-0.7582	0.8805	-0.9761	0.3200	0.5743	0.1284	-0.8812	-0.8647	-0.3060	-0.3915	-0.4272	0.1915	0.2144	-1.4450	1.4200	-0.4814	-0.2096	-0.9784	-0.0135	-0.0077	-0.2140	-0.1266	0.0076	-0.7782	0.2122	0.6580	-1.0440	-1.3020	0.3339	0.0169	1.5240	-0.1221	-0.4329	-0.7958	-0.0037	0.0000	-0.3257	0.6582	-0.7441	-0.3971	0.5478	-0.0122	-0.3485	-0.8992	0.0357	-0.7169	0.3069	0.3516	-1.2170	-0.8767	0.6026	-0.5114	0.2630	0.3713	0.1427	-0.0995	0.7796	-0.0396	-0.2313	0.9942	1.3830	0.0134	-0.8873	-1.1160	-0.2697	-0.6888	0.4901	-0.8713	-1.4200	0.2382	-0.0604	1.2890	-0.4723	-0.2206	-0.2513	0.5120	-0.2192	-0.1935	0.7454	0.2182	0.3303	1.4200	-0.5278	0.2120	1.1220	0.4979	-0.5965	-0.7072	0.0000	0.7770	0.3131	-0.9097	-0.2374	0.5918	-0.8645	-1.0270	0.0558	-0.8514	-4.1060	-0.1670	-0.2964	-0.7647	-1.3840	0.3288	1.8990	-0.6155	0.0303	1.1820	-0.8397	0.3528	-0.7332	-0.1398	1.5350	0.1255	0.4976	-0.2584	-1.6470	0.3688	0.0395	1.5880	-0.6069	-0.0381	-0.4890	0.8667	-0.0722	0.5782	-0.5289	-0.5300	0.7948	-0.0032	1.3890	0.0481	-0.5063	0.0751	-0.6765	-0.6041	-1.0960	0.2314	-0.5681	-0.5510	0.8730	1.2090	-0.6599	-0.0473	-0.1993	-0.0856	-0.3871	1.9380	-1.3640	-0.1179	0.2007	0.2311	0.3266	0.8366	0.3772	0.1381	-1.0850	0.1850	0.2187	0.8077	0.6426	1.0250	-0.8547	1.0490	0.3372	-0.4449	0.4069	0.4405	0.1495	-0.6211	-0.0926	0.3887	-0.5829	0.2234	0.4895	0.9147	0.1747	0.2022	1.5400	1.1010	1.1470	0.1393	-0.6369	-0.4644	0.3763	-0.4496	-0.1462	0.1622	-0.7715	0.2490	0.1726	0.6805	0.3083	-0.2181	-0.1990	-2.1710	0.8879	-0.3761	-0.3086	-0.2488	-0.0362	0.5528	-3.0580	0.4225	-0.3213	0.4988	0.5773	0.3258	-0.5311	-0.5035	0.2205	0.0488	-0.8068	-0.0898	-0.2314	-0.3970	0.4198	0.0848	0.9851	-0.7109	-0.3549	0.2496	-0.0743	0.0643	0.0000	-0.5595	-0.4818	0.4739	-0.7345	1.3710	-0.6181	0.8186	0.1090	-0.3756	-0.7728	-1.0210	0.6111	0.4759	-0.0862	-0.2117	0.2625	0.2349	1.1460	-0.1912	0.5670	0.3467	0.7070	-0.7589	0.6233	0.6787	-0.1529	0.0036	-0.2251	1.0070	0.3051	0.4352	-1.0340	1.1280	-0.1387	-0.6920	-0.0614	-1.0100	0.4272	0.7192	-0.1956	-0.6291	-1.0720	-0.3110	0.3025	0.3656	0.7580	-1.7020	0.1075	0.7784	0.0297	-0.4552	0.1246	0.7098	-0.1093	-0.7915	-0.4950	-1.1500	-0.3659	-1.8030	-1.3760	0.1950	0.1612	-0.9473	0.0607	0.5340	0.9504	-0.3440	-0.0452	0.6901	-0.6388	-1.7160	-0.2793	0.1120	-1.0640	0.5505	0.0084	1.1140	0.5001	-0.3571	4.8680	0.0387	-0.1652	0.9826	-0.7373	0.6854	-1.5550	-0.9811	0.5727	-1.0920	1.0880	0.3518	-0.7130	-0.3599	0.0045	0.7766	0.5204	0.6017	-0.7846	-0.7348	-0.7035	-0.5679	0.8118	0.7906	-0.6206	1.0330	-0.1025	-0.8553	0.6000	-0.1107	1.2910	-1.1060	0.6600	0.0499	0.0384	0.2105	-2.6580	-1.1190	0.4782	0.0000	0.5543	-0.1867	0.0000	-0.7533	-0.1708	1.0520	0.5713	0.3639	-0.1564	-0.2237	0.6563	0.6978	-1.1630	0.9847	1.1770	0.2059	0.6561	-1.1100	0.9577	-0.7402	0.5668	-0.1646	-0.1971	0.5430	-0.0838	0.8141	-0.2086	-0.5702	0.8300	0.1213	-0.9823	0.4846	-0.8438	-2.7170	0.4643	-0.4216	-0.4873	0.3106	1.2620	0.1443	0.6475	-0.7860	1.1540	-0.7091	-0.0532	1.0490	-0.8812	-0.1203	-0.6154	0.3791	-0.4260	1.1900	0.3423	0.2776	0.0856	-0.3366	1.0610	-0.1837	0.6845	-0.1154	-0.5802	0.1773	0.7461	0.7750	-0.0772	0.2560	0.3512	0.2229	-0.3598	-0.3864	-0.9980	0.2641	-0.5620	-0.2027	-0.0857	-0.0378	0.3320	-1.0120	0.6467	0.5676	-0.0162	0.2511	0.1317	-0.1299	0.7671	-0.1206	-0.4685	2.6110	0.0142	-0.4917	-0.6556	0.6252	0.6725	0.0789	-0.3372	0.7544	0.4120	0.3569	2.6050	-0.2693	0.3297	1.0720	-0.2280	-0.0969	-0.6763	0.0683	0.0614	-0.0215	0.1453	-0.7244	-0.1352	-0.5394	0.1322	1.6100	-0.7458	-0.7564	-0.0652	-0.0343	0.7695	-0.5814	-0.9151	-0.6114	0.3211	0.8205	-0.5881	-0.4215	0.9122	0.9490	-0.7509	-2.6020	-0.5051	-0.4627	-0.2087	0.4971	0.2445	0.1991	0.5453	0.5710	-1.0210	1.7320	0.8589	0.2774	-0.6752	-0.4742	-0.2575	0.1521	-0.7664	0.8195	-0.6218	0.2716	0.6445	-0.1963	-0.0558	1.1570	-0.8047	-0.0801	-0.0247	1.1720	0.0718	-0.4977	-0.0301	-0.2188	-0.2424	0.4021	0.1018	0.5486	0.2255	0.1477	0.7155	-0.0393	0.2462	0.4957	-0.4297	0.2417	0.5472	-1.8610	0.0002	1.1180	0.5149	0.1358	0.0761	0.4792	-0.0546	0.6184	-0.4246	-0.3559	0.1227	0.5769	0.8214	0.4973	0.6206	0.2147	0.2423	1.1830	0.9780	-0.0733	-0.9028	-0.5537	0.7440	0.1506	0.4542	0.1341	-0.4105	0.4757	-0.0707	-0.6718	0.0699	0.9190	0.4874	0.2594	0.3686	1.0850	-0.7241	0.2870	-0.0111	0.7355	0.3963	-0.0355	0.3651	0.5687	-0.1913	0.3042	0.1856	0.8670	-0.9542	-0.2257	-0.4909	0.1207	0.0226	0.4621	0.1636	0.3442	0.6127	0.7531	0.5940	0.3929	0.4998	-0.3026	-0.3105	0.8913	0.5339	0.1938	0.0717	1.2190	-0.2385	0.6439	-0.0766	-0.1473	-0.7318	0.0123	-0.2603	-0.1796	0.1324	0.1422	-0.4700	1.1780
21477	id_e638db82b	trt_cp	72	D2	0.2426	-0.8164	0.3878	-0.4919	-0.1288	-0.4755	0.3727	0.6090	0.4283	0.6229	0.1592	-0.5908	5.0000	-0.1125	-0.7181	-0.2614	0.4417	-0.1587	-0.6170	0.1724	-1.0490	0.9107	-0.6431	0.3617	-0.1989	-0.7050	0.0379	-0.7734	-0.6653	-0.0925	-0.3270	0.0205	0.5614	1.0160	-0.4401	0.3105	-0.1793	0.5997	-0.3793	1.3600	-0.0233	-0.0080	0.7427	-0.4656	0.4353	-0.0069	-0.6154	-0.0135	-0.2131	-0.3449	-0.3501	0.0285	-0.1162	-0.4692	0.6285	1.2340	-0.2438	-0.2632	0.2705	0.6387	0.3673	-0.1080	-0.5902	0.5562	0.0029	0.1256	0.4397	-0.6856	0.4413	0.1926	1.3020	0.3846	0.4090	0.1868	-0.5255	0.4789	-1.0440	0.0630	-0.6475	0.3960	0.5877	0.3240	-0.9151	-0.5599	-0.8554	0.0270	0.2813	0.2161	-0.0689	0.1085	-0.2200	-1.2480	0.5383	-0.2997	-0.4748	-0.0311	-0.2519	-0.3025	-0.6406	-0.5814	0.7795	-0.5614	0.5504	-0.7344	1.2880	-0.2677	-1.3840	0.2142	-0.4943	-0.0621	0.2406	0.1950	-0.6567	-3.9280	0.1591	-0.5827	-0.6938	0.5936	0.1628	0.1027	-0.3885	0.0641	0.7621	-0.9484	-0.0624	0.2890	-1.0220	-0.4339	0.2118	-0.1997	-0.2837	-0.4878	0.4922	0.2140	-0.3755	-0.8314	0.3264	-0.6967	0.5691	-0.8353	0.3729	-0.2677	-0.3315	-0.6219	-0.5157	0.8237	-0.5071	0.9599	0.9582	-0.4083	-0.1211	-0.8195	0.0107	0.0000	-1.0860	0.7336	-0.3431	0.2216	0.5714	0.1719	-0.4374	-0.1634	0.7706	-0.9324	-0.6141	0.0707	-0.3226	-0.4599	0.5165	1.7470	-0.7321	-0.0738	0.3432	0.2786	-0.8133	4.4180	2.1500	-0.8821	1.3600	-0.7717	-0.1346	-0.7930	0.2017	-0.9960	-0.1622	0.1379	-0.2050	0.5996	0.2490	-0.4412	-1.8850	1.3570	-0.3299	0.0872	-0.5393	0.0602	0.9467	-0.3925	0.0015	0.0987	-0.6635	0.6448	2.2300	1.4480	0.7329	-0.3210	-0.1833	-0.8034	-1.1110	-0.9646	0.1093	0.0835	-0.2667	-0.3372	1.4420	0.2850	0.4715	0.1586	-0.0765	0.0000	-0.5684	-0.1826	-1.0810	-0.4629	-0.7750	-1.0020	-0.2068	-0.4604	-0.3022	-0.3257	-0.7074	-0.3699	0.6120	-0.4590	-0.1736	-0.0759	-0.4266	-0.9507	1.2360	-0.4617	-0.3925	0.0410	0.6838	0.3094	-0.3147	0.5134	-0.3003	-0.5509	0.6237	-0.3098	-0.3850	0.9561	-1.9470	0.2292	0.6271	-1.1780	-0.5484	-0.0373	-1.2500	-0.3461	0.6915	-0.9130	-0.7879	-0.2952	0.1582	1.3290	0.0838	-0.1895	1.6220	-1.0480	0.0100	0.9135	0.6680	0.4119	-0.3390	0.5689	0.8818	0.2219	-0.4196	-0.2536	-0.2262	0.1516	-0.6580	-0.4072	-1.1960	-0.2496	0.4798	-0.9085	-0.4834	-1.9190	0.2908	-0.9900	0.8018	-0.0268	0.0732	1.6630	-0.3100	-0.5689	0.2960	0.9963	-1.2910	0.8738	-2.1730	0.0576	-0.3304	-1.0440	-0.4628	-0.4118	-0.3766	0.1405	0.0981	-1.4860	0.0805	0.8227	0.0137	-0.5153	-0.6226	-0.6352	-0.6411	-0.4907	-0.4833	0.3002	-0.2108	-0.4610	-0.4167	-2.3070	-0.0511	0.3338	0.3586	0.1407	-0.1722	-0.4532	0.2221	-0.2415	-0.2176	-0.2069	0.8904	-0.2496	-1.0000	0.8962	0.3484	-0.6155	0.5793	0.8215	-0.6108	0.1653	0.2964	-0.6847	-1.6590	0.0892	1.3190	-0.1726	0.1014	1.1800	-1.2710	-0.4729	-0.6898	0.4183	0.1777	-0.7332	-0.1139	-0.0809	-0.1374	-0.7649	-0.4607	0.7499	-0.3372	-0.3553	-0.0549	-1.0730	0.4178	-0.2446	0.3523	0.6802	-0.0640	2.2720	0.8188	0.8748	-0.6927	-0.4638	-0.3272	0.3321	0.5197	0.1364	-0.7652	1.3950	0.2242	0.3577	-0.9436	0.1320	1.4230	0.7383	0.1922	0.1771	-1.1560	0.2670	-0.9581	0.4511	-0.5724	0.9940	-0.1156	0.0990	0.1650	-0.2764	-0.1420	0.7251	-0.7817	-0.7419	-0.5961	-0.6321	-0.0157	0.8113	0.8188	0.6722	0.9958	-0.2806	1.6520	0.3149	0.1887	0.4130	-0.5939	0.2195	0.2129	-0.9757	0.8807	1.5360	-0.7659	0.4901	0.6438	-0.6020	0.0798	-0.2449	-0.2792	0.5039	-1.0790	-0.3005	-0.3152	0.4927	0.5256	-0.2226	0.5677	1.5670	-0.4332	0.8630	0.4840	-0.6011	1.3320	-1.1120	-1.0750	0.8727	0.0124	-0.0956	-0.7528	-0.0955	-0.5782	0.1314	0.5646	-0.6788	-0.1372	-1.4570	0.0362	0.3726	-0.5484	0.0196	0.1069	-1.3710	-0.4304	0.5822	-0.4067	0.8282	-0.1796	-0.6451	-0.1312	-0.2887	-0.8094	1.0860	-0.0450	-0.1285	0.2203	-0.3261	0.4428	0.4497	-0.5824	0.3396	0.2768	0.7134	-1.6070	0.3578	-0.2833	0.0816	1.0790	-0.0459	-0.0815	0.1905	0.2888	-0.3298	-0.5110	0.4123	-1.1870	-0.1093	0.4597	-0.0548	0.5227	-0.4031	0.2263	3.2480	-0.7241	0.5787	-2.2230	-0.3425	-0.2325	-0.0082	-0.3072	-0.5732	1.2760	-0.2686	-0.4839	0.0006	-0.3740	0.0033	-1.4030	0.7295	-1.2860	0.1242	0.0216	0.1803	-0.1299	-0.5522	0.5867	-0.0428	-0.9754	-0.0631	0.4558	2.4830	0.2718	-0.3944	-0.2928	-1.3380	-0.7901	-0.6095	0.9096	-0.0857	0.6258	0.3913	1.4710	-1.2030	-0.1262	0.2090	-0.4171	0.9150	-0.7423	-0.5347	-0.2630	-0.8359	-0.0930	-0.0939	-0.5056	-0.7473	0.1806	-0.6762	0.0464	-0.7272	-0.7530	-0.1486	0.2106	-0.5343	0.1989	0.1892	-0.7582	-0.8360	-0.6204	-0.0450	1.0240	-0.7451	0.1286	0.0544	0.7950	-1.1830	-0.1072	0.3741	-0.2163	0.7104	-0.5809	0.5095	-0.3438	0.3044	-0.3247	-0.0209	-0.2742	-0.2352	-0.7096	-2.3830	0.8789	0.1991	-0.2715	0.0702	-0.9578	0.6553	0.6960	-0.6396	-0.3494	0.7233	1.3030	-0.5445	-0.2190	-0.3416	-1.3500	0.9286	-1.0900	-0.0027	-2.0470	-0.6023	-1.1570	-0.2486	0.7414	-0.0202	0.5585	-0.6205	0.1705	-1.1810	1.6960	-0.1208	0.8922	-1.3250	0.5402	-0.7037	-0.6614	1.1900	4.1570	0.1495	-0.2657	0.5010	-0.2998	-0.2101	0.0520	-0.9941	-0.0132	0.2502	-0.3853	1.0480	-0.0628	0.4199	-0.3462	0.6263	-0.1524	-0.9747	-0.2993	-0.4843	-0.3557	-2.4480	-0.6271	0.7930	-0.9372	-0.7865	1.0340	-0.3454	-0.3330	-0.7318	-0.0226	-0.9206	0.1277	0.0017	-0.5157	-0.1374	-0.2753	-0.2147	0.0387	-0.2975	-0.2145	-0.4716	-0.6465	-0.1302	-0.7234	-0.5037	0.5556	-0.5055	0.4050	-0.5275	1.1670	-0.1119	-0.2468	-0.0143	-0.2542	0.7750	-0.4135	-0.1686	-1.0440	-0.1072	-0.1693	0.9352	-0.4592	0.5196	0.0223	-1.8330	0.2903	0.1672	0.3680	-0.0681	-0.8882	-0.3871	-0.2479	-0.4076	-0.4931	0.2531	0.2161	-0.3620	-1.1230	0.0000	0.8166	-0.7129	0.6825	0.4230	-0.5944	1.6510	-0.1400	0.8262	-0.0391	-0.5447	0.1551	1.7220	1.1590	-0.0453	0.1653	-0.4581	0.0572	1.0410	-0.4674	0.6718	-0.0907	-0.4441	-0.2256	-0.0413	-0.0148	0.4132	-0.5645	0.0893	0.1843	-0.3661	-0.3551	-0.4075	0.7038	-0.0502	-0.9670	-1.4690	-0.5755	0.3925	-0.0982	0.6777	-1.2270	-0.1707	-0.6086	-1.0910	-0.8893	-0.4640	-0.4597	2.3770	-0.1100	-0.0678	-0.2149	-1.7370	0.1042	1.2540	-0.0497	0.1814	0.7928	1.2100	0.9613	0.0362	-0.7406	0.4566	0.1702	-0.6848	0.1555	0.4769	0.0922	0.4536	0.4191	0.4998	-0.2158	-0.9678	-0.6634	0.2492	0.1460	0.2662	0.1015	-0.0548	1.0880	-0.2598	0.0985	-0.1236	0.1460	0.9637	0.2797	-1.3090	0.4312	-0.4455	0.0591	-0.7249	0.6380	-0.6800	-0.1955	-0.4327	-0.3581	0.4872	0.3833	0.0668	0.2238	-0.4716	0.3078	-0.2729	0.0247	-0.6818	0.6607	-0.0751	0.4145	0.5914	-0.2304	0.4900	1.5580	-0.7633	0.6089	0.0875	0.5134	0.3458	0.6991	0.4362	0.0747	-0.1342	-0.7805	0.0236	0.4344	-0.5074	-0.1638	-0.0441	0.0254	0.1320	-0.5309	-0.1943	0.5281	-0.4945	-0.3776	-0.4156	1.1000	-0.4360	0.8477	0.2282	1.4180	0.2782	-0.6536	0.0141	0.6710	0.9032	-0.5718	0.6070	-0.1024	0.1312	-0.4016	0.1634	0.1409	-0.6366	-0.1602	-0.5592	0.6380	-0.0560	-0.2072	0.4151	0.5736	-0.6160	0.2630	-0.1393	-0.1215	1.1610
3547	id_262b1d2f0	trt_cp	72	D1	-0.5750	-0.5393	-0.8351	0.3157	0.0205	0.6412	0.4954	-0.3068	0.1663	-0.3899	0.2347	-1.1370	0.0000	0.1373	0.7267	1.2280	0.2023	0.1700	-0.0405	-0.7116	0.9582	2.2800	-1.0950	0.0000	0.4214	1.5560	0.2377	1.4720	0.3851	-0.2684	0.9558	0.5146	0.4238	-0.6745	-0.1337	0.8310	-0.2539	-0.0954	-1.1670	-0.7878	0.5569	-0.9799	0.4996	0.4210	-1.9480	0.9278	0.0000	-0.3204	1.1440	-0.1690	0.6613	-0.7244	-0.9982	-0.3036	-0.1473	-0.3834	0.4224	-0.2542	-1.7530	1.4640	-0.7560	2.4430	-0.3658	0.2536	1.4960	0.5161	-0.4900	1.0520	-0.5728	-0.2900	0.3638	0.5389	0.2407	-0.1049	-0.2244	-1.0450	0.7717	0.1019	1.4180	-0.7426	0.9681	-1.3220	-0.7432	-0.7067	0.1917	-1.4010	-0.0452	-1.0390	0.0735	0.4521	-0.4105	0.0000	0.4771	0.2871	0.4715	-0.4286	-0.2332	0.6711	-1.1500	-0.3611	0.2504	-0.6188	-0.0271	-0.8354	0.3116	0.0853	-0.6745	1.2850	0.8672	0.5598	-0.1484	-0.1799	-0.5721	0.2843	-0.8424	-0.0381	-1.7710	-1.0430	-0.8522	1.1930	-0.0242	0.0862	0.1464	-1.3650	-0.9842	0.2682	0.2113	-0.3575	-1.1490	-0.0998	0.4162	-0.0354	0.0000	-0.6393	-1.4330	0.1423	0.5694	5.8540	0.0000	0.4570	0.0854	-1.2680	0.0544	-0.4369	-0.1122	-1.3240	-1.1620	-0.2496	0.3437	-0.0532	0.5759	0.4870	0.9201	-0.5713	-0.4504	-0.3484	0.5076	-1.1070	-1.0350	0.4164	1.8000	-0.3609	-1.1800	-0.1926	-1.0240	-0.0341	0.5215	-0.5585	-0.1415	0.3485	1.0050	0.8794	0.4970	-1.0600	-1.3900	0.6239	-0.4223	0.0000	-0.3734	0.3057	-1.6860	0.4960	-0.7091	0.3513	-0.6116	0.3972	-0.2854	0.9320	-0.2406	-0.3504	-1.1600	0.8366	-0.4715	-1.0660	-0.7494	-0.0611	0.8380	0.4931	0.2289	-0.1935	-0.2691	-0.6226	-0.3243	-1.1700	0.6537	-0.2150	0.0000	-0.5376	0.2129	0.4552	-1.0330	1.1050	0.1504	-0.3116	0.3711	1.2120	0.1464	1.0240	0.3580	0.0000	-0.4753	0.3080	0.3654	-0.8778	0.1670	-0.6507	-0.2597	0.4424	0.5787	-0.2198	0.2312	0.1171	-0.3415	0.3938	-0.1410	-0.3134	0.0000	0.6887	0.0000	1.1470	-1.4090	0.0000	-0.5128	-0.9942	-0.2489	-0.2661	0.4347	-0.3950	0.6250	-0.5697	0.9948	-0.6338	-0.2043	-0.2759	0.7060	0.0684	0.9857	1.3850	-0.0550	-0.8141	0.5562	-0.9571	0.7685	-0.0961	0.0767	0.9198	0.9523	0.0765	0.1550	-0.7641	0.5908	-0.9928	0.6745	-2.0800	0.6745	0.2779	-2.4610	0.3659	0.0000	-0.6750	-0.2762	-0.4996	0.7881	0.0731	-2.0710	0.4306	0.7066	0.3288	0.6131	-0.3831	0.1555	0.4478	0.4883	-0.1582	-0.7966	-0.4309	-0.5895	0.0000	0.3758	0.6314	-0.1818	1.2090	-0.7695	-0.6852	1.9810	0.1229	0.1723	0.0000	0.1205	1.0740	0.4789	0.4163	0.2798	-0.3454	1.2510	1.1890	0.8237	-0.3872	0.6121	-0.7090	0.4585	-0.8466	-0.4485	0.0242	-0.1686	0.5054	0.2516	0.1015	-2.1640	-1.1380	0.6698	-0.3433	0.1268	0.0000	-0.2233	-0.7999	0.1987	-0.6623	-0.7389	1.4230	-1.5000	0.1941	10.0000	-1.3210	1.7270	0.2604	-0.8057	0.9126	-0.2077	-1.1760	-1.3080	1.6870	1.3280	-0.8066	0.1693	-0.5788	0.0000	0.9157	0.1674	-1.3350	3.0080	0.2283	-1.7670	0.5592	-0.3301	-0.6745	0.2432	-1.4530	-0.8411	-0.8837	0.0000	0.0000	-0.7197	0.0871	0.5694	0.1281	-1.7140	0.0000	-1.6110	0.0000	-0.5061	1.3660	0.4968	-0.0276	1.0760	0.2840	0.0989	0.0000	-0.3501	-0.4707	0.2965	0.0936	0.0000	-0.2678	0.8484	1.5740	1.0200	1.1890	-1.4410	0.4281	0.5458	0.6402	0.1584	-0.2520	0.4575	-1.5770	-0.6927	-0.0857	0.2540	-0.3777	0.2176	-1.1360	0.1194	1.4840	0.2360	0.2741	0.4136	-1.8640	-1.7320	0.0327	0.7935	0.5305	-0.0423	-1.4870	0.0000	-1.6990	-0.4811	0.5021	-0.6402	-0.0620	-0.6479	-0.7094	-1.0400	2.0850	1.0910	1.2860	-0.4954	-0.3025	-0.4845	1.1590	0.8587	-0.6348	0.0000	-1.7580	0.7839	-0.5493	0.3681	-1.4080	-0.3596	0.2145	-0.1852	-1.0510	2.0120	-0.4853	0.7383	0.4794	-1.1600	0.7325	0.5105	-0.2954	-0.9173	-1.9510	0.9393	-0.0698	-0.7309	1.7750	-0.0843	1.3130	-0.2181	0.5792	-0.9750	-0.2573	-0.0489	0.4204	0.0728	-1.3780	-1.1230	0.7994	-1.0730	1.2090	-0.4719	0.0000	-0.2757	-0.0548	-1.2130	0.8301	0.2305	-0.2846	-2.5090	-0.0989	-0.4009	-0.3419	0.6265	-0.6745	-0.4411	0.1614	0.2975	1.1340	0.1735	0.2869	-1.9200	-0.9996	0.0544	0.3873	0.2692	-0.6745	-1.3140	0.7513	0.7542	0.0719	0.6745	-0.4138	-1.8790	-0.8274	-0.3218	-0.8446	-0.4915	-0.1514	-0.3212	-0.8683	2.4230	-0.8903	0.9540	0.3558	0.0829	0.5515	-2.7590	-0.3300	-1.2160	-1.0480	-1.1170	0.2523	1.0600	-0.6791	0.1707	-1.0810	0.2543	0.2727	-0.6286	-0.7104	-0.3106	-0.5922	0.4430	-1.4730	-0.0450	0.8962	0.5842	-0.4688	-0.7898	0.5725	0.1153	-0.7492	0.5007	0.7377	0.4225	0.6745	-1.1130	0.2327	-0.6036	0.4293	1.2160	0.5666	-1.6630	-0.7264	-0.9707	0.5048	1.1690	-0.1540	0.1482	-0.2705	1.1000	0.8036	0.7090	-0.0567	1.2230	-0.3219	0.4809	-0.3845	-0.6242	1.4950	-0.3332	-0.8001	-1.4110	0.6259	1.3810	0.3933	0.6357	-0.8624	-0.2120	-0.5796	-0.0600	-0.3347	3.8280	0.8031	-1.2200	-0.3710	-1.3620	-1.4220	-0.0823	-0.8950	-0.5693	1.2280	-1.2880	-1.4450	-0.5294	-0.7102	-0.7697	-2.4890	-1.3650	-1.0220	-1.8780	-0.3797	-0.4456	-0.2901	-1.5380	1.0470	1.2900	-0.1533	-0.1801	-0.2475	-1.2560	0.1392	-2.3180	-0.8532	-0.2494	-0.2242	0.6745	-1.3270	-0.9370	-1.0290	-0.1039	0.2394	1.0000	-0.5936	-0.6745	-1.4570	0.6745	0.7189	-1.5050	-0.2695	0.9287	0.4924	-2.2830	0.7376	-0.1675	-0.3840	0.7849	-0.7628	-2.2920	0.9141	-0.4311	1.2810	0.7406	1.0210	-0.5520	-1.0660	0.1150	0.4609	-0.1764	0.0509	1.1210	-0.6292	1.1600	-1.2360	0.8426	-0.8662	1.4950	0.0000	0.7542	-0.2330	0.6429	-0.7555	0.1977	-1.0310	-0.3850	-0.2659	-0.1929	1.2910	-0.1438	-1.0480	1.0770	0.2992	-0.5158	0.4678	-1.7480	-0.3119	-1.0130	0.6745	-0.1949	0.1320	-1.0280	-1.4640	1.2710	0.8070	-0.3139	-0.4508	-0.8006	-0.1207	-0.0995	0.7680	1.1840	0.8600	-0.7310	-0.1973	0.4661	0.5355	-0.5963	0.0000	-1.0050	-1.0710	0.0911	-0.9093	1.0540	-0.6832	-0.3412	-5.2580	-0.6156	0.8792	0.0000	1.2760	-0.3917	-0.8519	-0.3777	-0.8659	0.4984	0.0769	0.6745	0.3255	-2.6300	0.0591	0.2359	0.7550	-0.6506	0.0000	0.8316	0.1291	-0.1542	-0.6168	-0.4065	0.3583	0.2388	-0.4634	0.5364	-0.1044	1.3940	-0.1712	1.3530	-1.0120	0.3624	1.4130	0.1551	-0.5872	-0.7192	10.0000	-0.1760	-0.9165	0.0830	0.7107	-1.3890	-0.0818	0.2322	0.6207	-0.6745	-0.6756	-0.8390	-0.2425	-0.1187	-0.4535	-0.0614	0.3033	-0.6781	-0.5560	1.2200	-0.2192	-0.1514	0.6124	0.4108	0.7138	0.2006	-0.4904	0.4125	-0.0468	0.4078	-0.6591	-0.1798	-0.2661	0.1692	-0.8042	-0.6479	-0.5064	-0.2624	-0.4214	-0.7602	0.1776	0.0126	0.7492	0.0815	0.5122	-0.0345	-0.1702	-0.2538	0.1175	0.7298	-0.0085	-0.0995	-0.3705	-0.5755	-0.3649	-0.5034	0.7978	-0.4060	0.8173	-0.7958	-0.2021	0.6548	-0.3159	0.4589	0.7756	-0.0540	0.1093	0.0639	0.2545	0.3795	-0.1146	0.4846	-0.2705	0.4439	-0.5269	-0.7589	0.3757	0.2137	-0.3353	0.7949	-0.0744	0.2498	0.4709	-0.7295	0.3248	0.9315	-0.3754	0.4629	-0.2490	-0.1124	-1.0370	-0.4016	-0.2194	0.9992	-0.5649	-0.4206	0.6646	0.1466	0.7451	0.1357	0.0177	0.3563	-0.5098	-0.9224	0.4322	0.4109	-0.1896	-1.0250	0.0972	0.7310	0.7389	-0.0988	-0.7663	-0.0930	0.3485	0.1791	0.3335	0.6364	-0.3984

test_features.head()

	sig_id	cp_type	cp_time	cp_dose	g-0	g-1	g-2	g-3	g-4	g-5	g-6	g-7	g-8	g-9	g-10	g-11	g-12	g-13	g-14	g-15	g-16	g-17	g-18	g-19	g-20	g-21	g-22	g-23	g-24	g-25	g-26	g-27	g-28	g-29	g-30	g-31	g-32	g-33	g-34	g-35	g-36	g-37	g-38	g-39	g-40	g-41	g-42	g-43	g-44	g-45	g-46	g-47	g-48	g-49	g-50	g-51	g-52	g-53	g-54	g-55	g-56	g-57	g-58	g-59	g-60	g-61	g-62	g-63	g-64	g-65	g-66	g-67	g-68	g-69	g-70	g-71	g-72	g-73	g-74	g-75	g-76	g-77	g-78	g-79	g-80	g-81	g-82	g-83	g-84	g-85	g-86	g-87	g-88	g-89	g-90	g-91	g-92	g-93	g-94	g-95	g-96	g-97	g-98	g-99	g-100	g-101	g-102	g-103	g-104	g-105	g-106	g-107	g-108	g-109	g-110	g-111	g-112	g-113	g-114	g-115	g-116	g-117	g-118	g-119	g-120	g-121	g-122	g-123	g-124	g-125	g-126	g-127	g-128	g-129	g-130	g-131	g-132	g-133	g-134	g-135	g-136	g-137	g-138	g-139	g-140	g-141	g-142	g-143	g-144	g-145	g-146	g-147	g-148	g-149	g-150	g-151	g-152	g-153	g-154	g-155	g-156	g-157	g-158	g-159	g-160	g-161	g-162	g-163	g-164	g-165	g-166	g-167	g-168	g-169	g-170	g-171	g-172	g-173	g-174	g-175	g-176	g-177	g-178	g-179	g-180	g-181	g-182	g-183	g-184	g-185	g-186	g-187	g-188	g-189	g-190	g-191	g-192	g-193	g-194	g-195	g-196	g-197	g-198	g-199	g-200	g-201	g-202	g-203	g-204	g-205	g-206	g-207	g-208	g-209	g-210	g-211	g-212	g-213	g-214	g-215	g-216	g-217	g-218	g-219	g-220	g-221	g-222	g-223	g-224	g-225	g-226	g-227	g-228	g-229	g-230	g-231	g-232	g-233	g-234	g-235	g-236	g-237	g-238	g-239	g-240	g-241	g-242	g-243	g-244	g-245	g-246	g-247	g-248	g-249	g-250	g-251	g-252	g-253	g-254	g-255	g-256	g-257	g-258	g-259	g-260	g-261	g-262	g-263	g-264	g-265	g-266	g-267	g-268	g-269	g-270	g-271	g-272	g-273	g-274	g-275	g-276	g-277	g-278	g-279	g-280	g-281	g-282	g-283	g-284	g-285	g-286	g-287	g-288	g-289	g-290	g-291	g-292	g-293	g-294	g-295	g-296	g-297	g-298	g-299	g-300	g-301	g-302	g-303	g-304	g-305	g-306	g-307	g-308	g-309	g-310	g-311	g-312	g-313	g-314	g-315	g-316	g-317	g-318	g-319	g-320	g-321	g-322	g-323	g-324	g-325	g-326	g-327	g-328	g-329	g-330	g-331	g-332	g-333	g-334	g-335	g-336	g-337	g-338	g-339	g-340	g-341	g-342	g-343	g-344	g-345	g-346	g-347	g-348	g-349	g-350	g-351	g-352	g-353	g-354	g-355	g-356	g-357	g-358	g-359	g-360	g-361	g-362	g-363	g-364	g-365	g-366	g-367	g-368	g-369	g-370	g-371	g-372	g-373	g-374	g-375	g-376	g-377	g-378	g-379	g-380	g-381	g-382	g-383	g-384	g-385	g-386	g-387	g-388	g-389	g-390	g-391	g-392	g-393	g-394	g-395	g-396	g-397	g-398	g-399	g-400	g-401	g-402	g-403	g-404	g-405	g-406	g-407	g-408	g-409	g-410	g-411	g-412	g-413	g-414	g-415	g-416	g-417	g-418	g-419	g-420	g-421	g-422	g-423	g-424	g-425	g-426	g-427	g-428	g-429	g-430	g-431	g-432	g-433	g-434	g-435	g-436	g-437	g-438	g-439	g-440	g-441	g-442	g-443	g-444	g-445	g-446	g-447	g-448	g-449	g-450	g-451	g-452	g-453	g-454	g-455	g-456	g-457	g-458	g-459	g-460	g-461	g-462	g-463	g-464	g-465	g-466	g-467	g-468	g-469	g-470	g-471	g-472	g-473	g-474	g-475	g-476	g-477	g-478	g-479	g-480	g-481	g-482	g-483	g-484	g-485	g-486	g-487	g-488	g-489	g-490	g-491	g-492	g-493	g-494	g-495	g-496	g-497	g-498	g-499	g-500	g-501	g-502	g-503	g-504	g-505	g-506	g-507	g-508	g-509	g-510	g-511	g-512	g-513	g-514	g-515	g-516	g-517	g-518	g-519	g-520	g-521	g-522	g-523	g-524	g-525	g-526	g-527	g-528	g-529	g-530	g-531	g-532	g-533	g-534	g-535	g-536	g-537	g-538	g-539	g-540	g-541	g-542	g-543	g-544	g-545	g-546	g-547	g-548	g-549	g-550	g-551	g-552	g-553	g-554	g-555	g-556	g-557	g-558	g-559	g-560	g-561	g-562	g-563	g-564	g-565	g-566	g-567	g-568	g-569	g-570	g-571	g-572	g-573	g-574	g-575	g-576	g-577	g-578	g-579	g-580	g-581	g-582	g-583	g-584	g-585	g-586	g-587	g-588	g-589	g-590	g-591	g-592	g-593	g-594	g-595	g-596	g-597	g-598	g-599	g-600	g-601	g-602	g-603	g-604	g-605	g-606	g-607	g-608	g-609	g-610	g-611	g-612	g-613	g-614	g-615	g-616	g-617	g-618	g-619	g-620	g-621	g-622	g-623	g-624	g-625	g-626	g-627	g-628	g-629	g-630	g-631	g-632	g-633	g-634	g-635	g-636	g-637	g-638	g-639	g-640	g-641	g-642	g-643	g-644	g-645	g-646	g-647	g-648	g-649	g-650	g-651	g-652	g-653	g-654	g-655	g-656	g-657	g-658	g-659	g-660	g-661	g-662	g-663	g-664	g-665	g-666	g-667	g-668	g-669	g-670	g-671	g-672	g-673	g-674	g-675	g-676	g-677	g-678	g-679	g-680	g-681	g-682	g-683	g-684	g-685	g-686	g-687	g-688	g-689	g-690	g-691	g-692	g-693	g-694	g-695	g-696	g-697	g-698	g-699	g-700	g-701	g-702	g-703	g-704	g-705	g-706	g-707	g-708	g-709	g-710	g-711	g-712	g-713	g-714	g-715	g-716	g-717	g-718	g-719	g-720	g-721	g-722	g-723	g-724	g-725	g-726	g-727	g-728	g-729	g-730	g-731	g-732	g-733	g-734	g-735	g-736	g-737	g-738	g-739	g-740	g-741	g-742	g-743	g-744	g-745	g-746	g-747	g-748	g-749	g-750	g-751	g-752	g-753	g-754	g-755	g-756	g-757	g-758	g-759	g-760	g-761	g-762	g-763	g-764	g-765	g-766	g-767	g-768	g-769	g-770	g-771	c-0	c-1	c-2	c-3	c-4	c-5	c-6	c-7	c-8	c-9	c-10	c-11	c-12	c-13	c-14	c-15	c-16	c-17	c-18	c-19	c-20	c-21	c-22	c-23	c-24	c-25	c-26	c-27	c-28	c-29	c-30	c-31	c-32	c-33	c-34	c-35	c-36	c-37	c-38	c-39	c-40	c-41	c-42	c-43	c-44	c-45	c-46	c-47	c-48	c-49	c-50	c-51	c-52	c-53	c-54	c-55	c-56	c-57	c-58	c-59	c-60	c-61	c-62	c-63	c-64	c-65	c-66	c-67	c-68	c-69	c-70	c-71	c-72	c-73	c-74	c-75	c-76	c-77	c-78	c-79	c-80	c-81	c-82	c-83	c-84	c-85	c-86	c-87	c-88	c-89	c-90	c-91	c-92	c-93	c-94	c-95	c-96	c-97	c-98	c-99
12276	id_843baf550	trt_cp	48	D1	-0.4058	-0.3321	-0.1978	1.7460	-0.2183	-1.2990	0.0056	-1.0930	0.6952	1.8400	0.5498	-0.7930	0.9326	0.1712	0.0859	-0.3463	0.8160	1.1290	0.3020	-1.8890	-0.0511	0.2796	0.5574	1.0110	-0.3384	0.0941	0.9491	1.0780	1.5410	-0.7920	1.0910	0.6661	-0.9051	0.7166	0.0474	-0.2384	0.4473	0.0736	-0.9734	-0.4039	0.6003	0.7901	0.0563	-1.7990	0.5673	-0.8153	-0.3338	0.4796	0.7707	0.5933	1.3650	-1.1110	-0.7970	0.3708	0.3556	1.1040	1.9260	1.8890	0.5931	0.2420	0.1477	-0.5256	-0.6276	-1.7700	-0.2573	-0.7463	0.3547	0.0670	-0.4575	0.0126	0.5709	-3.6790	0.7267	-0.8062	0.3378	1.1700	-0.3221	0.6616	-0.9348	0.0145	-0.0276	0.3689	0.9041	-0.9647	1.1670	-1.0790	-1.0610	-0.8789	0.6530	-0.2882	0.6712	-0.5035	0.4465	-0.1371	-0.9287	-0.9712	0.3329	-0.8139	0.3827	0.2928	-1.1590	0.9830	-0.5694	-1.5810	0.0000	1.6080	0.3372	0.3471	1.0720	-1.3780	0.6600	-1.0340	-0.7169	1.4320	1.4610	0.7541	-0.0352	1.1950	0.8315	-0.7375	-1.5770	0.0314	-0.1849	-0.1715	-0.3721	1.1050	-1.5840	1.2800	-0.8505	-0.8549	-0.6018	-1.3450	0.2184	0.1633	0.1604	0.0569	0.1251	1.1730	0.2656	-0.3192	-0.0262	0.2418	-0.5324	0.9987	1.1330	-0.5225	0.2482	-0.3724	-0.7538	2.2390	0.3916	-0.0549	0.6209	0.3228	0.1608	-1.5020	-0.1478	3.4350	-1.0830	0.7465	1.1280	-0.1267	-0.3502	-0.4858	-1.3360	0.3362	0.5724	1.5760	-0.4415	-1.1660	-0.2057	-0.8397	-0.8792	-0.2525	0.2215	-0.4658	-0.2038	-0.1752	3.2970	1.4090	-0.5163	-1.0600	1.6520	-0.2797	-0.0627	0.6987	1.7090	0.7335	0.2896	0.3573	2.9350	-0.5059	0.7444	-1.1840	0.8173	-0.9639	0.5177	-0.5660	-0.6953	0.7081	-0.1838	-0.5176	0.4200	0.0104	0.5234	-0.2468	0.7215	-1.1910	-1.0290	-0.0602	-0.5827	0.8643	-1.9090	0.0000	1.5780	0.9377	-1.3720	0.2011	0.3182	-0.0823	0.5580	-0.0062	-0.3694	0.5812	-0.1462	0.3291	0.1437	-0.8178	1.0590	-1.4160	-0.9223	-0.2168	-1.4580	-0.2721	-0.8847	-0.0295	0.0697	-0.0391	0.0000	0.2646	0.6001	-0.2626	-1.5160	-1.2090	0.5537	-2.1520	0.4749	-0.9222	0.3534	0.8405	1.4210	-1.3830	-0.0821	0.6070	0.7379	-0.1737	-1.4540	-0.5139	0.6718	1.1060	-0.3173	-1.8160	-0.3457	0.6396	1.5320	-0.2859	-1.0700	0.7649	-0.4133	0.5367	0.2269	-0.0621	0.6053	0.2500	0.0398	-1.2970	1.1200	-0.1611	-0.1204	-0.2980	0.7793	1.9970	1.5200	-0.5686	-0.1505	0.4098	-1.8010	-0.2173	0.6645	0.4843	1.0390	0.0277	0.2108	0.5143	0.2995	-0.4684	0.7526	0.1359	0.5035	-0.2661	-1.1770	-0.0093	0.5274	-0.2812	0.4202	-0.4431	-0.5929	0.0000	-0.8985	-0.4538	0.1072	-0.9462	0.2269	0.9881	0.2421	-0.9471	-1.1960	-0.5401	-1.0330	1.5320	0.0503	-0.3843	0.1631	0.0037	0.3555	-0.9353	1.2030	0.4874	-1.7880	-4.4290	-0.9318	-0.3046	0.2485	-0.6308	0.5469	0.5080	-1.1240	0.7488	-0.0406	-0.2107	-0.5998	-1.1610	1.6820	0.8240	-1.2160	-0.4348	0.9620	0.7394	-0.5145	0.6059	0.1532	1.0990	-0.5105	0.0333	0.8347	-0.4296	-0.9299	0.6885	0.1996	0.3372	0.0277	-0.7054	0.6344	0.2988	2.3070	-1.0610	-0.1845	-1.1070	1.2490	-0.1868	0.3372	0.4080	0.0428	-0.6302	-0.2601	-3.1190	1.5610	-1.6540	2.5720	0.3947	0.9991	-0.9523	-2.5380	-0.8656	1.0790	0.6148	0.0720	-0.9782	-0.7229	0.2199	0.1063	-0.4942	0.3950	-1.4180	0.5958	0.6009	-0.4401	-1.8940	-0.3260	0.5121	0.9725	-0.3159	0.2240	-0.6844	-0.8981	0.2179	1.2840	0.4732	-0.6542	-0.5053	0.0321	0.9925	-0.5964	0.1857	0.3717	-0.0221	-0.1740	-1.1450	-0.9882	0.0495	-0.1312	-0.1156	-1.3210	0.3189	-0.3566	-0.0925	0.0396	-1.0310	-0.1994	-0.5273	0.6783	-0.7781	0.1400	-0.8527	0.2335	0.3180	0.6309	-1.1090	-0.2841	0.8247	-1.3090	-0.2831	1.3730	0.6155	-0.0727	-0.4028	0.1821	-0.4421	0.8659	0.7733	-0.3001	-0.0999	0.1639	0.0785	-1.3720	0.0232	0.9059	-0.8825	0.1644	1.2340	0.6033	-0.9198	-0.6678	-0.3419	-0.0670	2.3990	-0.8220	-0.6946	-0.7592	-0.3898	-1.7890	-1.8150	-1.9260	0.6587	0.5910	0.2301	0.7019	-0.8836	-0.1850	0.9795	-1.0400	0.6287	0.0277	-0.5074	-0.5310	-0.5296	-0.6717	-1.1390	0.0698	0.1953	-0.8745	0.4899	-0.0704	-0.0852	-1.1770	-0.7669	-0.1089	-0.8149	0.0398	-0.5193	-0.4374	0.5845	-0.5845	0.1536	-1.9010	0.7028	0.3198	-0.5256	1.1830	1.1600	0.9328	0.5324	1.0230	-0.4263	0.3136	-0.7558	0.2781	0.3814	0.2199	-0.7462	0.3651	-0.0319	0.4624	1.3620	0.4609	-0.3999	-0.3550	1.3040	-0.3729	0.5954	1.5800	-0.6080	-0.7006	-0.4059	0.5858	1.1310	-0.1538	-0.1617	-0.3440	0.8416	0.2880	0.3764	0.5589	0.1486	-1.1270	0.9105	-0.6992	0.5635	-1.1860	1.0110	0.0000	0.6022	-1.0280	-1.2510	-0.4006	-0.7815	-1.4430	0.7470	-0.1076	-1.1560	-0.8529	1.2830	0.7212	0.8244	-0.0178	1.0130	-1.8130	-0.0326	0.0335	-0.2546	-0.4951	0.4724	0.1181	-0.1870	-0.6280	0.1837	-1.0300	-0.8956	0.3736	0.3702	0.1976	-0.6494	-1.1100	0.6412	1.2940	0.5008	-0.3459	-1.0040	-0.7552	0.2048	-0.2030	0.0420	1.3640	-0.4176	-1.3150	-1.9200	-0.3417	0.3867	0.0965	-0.6291	0.6538	0.7765	1.1490	0.1423	-0.6774	-0.5132	0.7617	-1.0710	0.1668	0.9880	0.9324	-1.4680	-0.0043	-1.2800	-0.2443	0.1411	1.2590	-0.0243	-0.3372	0.0170	-2.5520	0.4178	0.0143	-0.3943	1.1240	0.3620	0.0396	0.0209	-0.1048	-0.3340	-0.2006	0.3123	0.4364	-0.2624	-0.0182	-0.7420	0.3089	0.2462	0.0769	-0.1650	0.1988	0.6323	-0.2008	-0.4484	1.4270	-0.4029	0.2749	0.5814	-0.2248	-0.1652	0.7333	-0.0709	0.6219	0.4955	-0.6161	1.9210	0.2585	-0.3361	0.6364	-0.2805	0.9973	-0.1203	-0.2137	-0.3612	0.0903	-0.4320	0.1842	-0.1435	-0.3146	0.3332	0.5112	0.1332	1.2920	0.6653	1.2360	0.2555	-1.0770	0.4029	0.8789	1.2200	1.1300	0.1976	2.0380	0.3564	-0.0614	-0.1326	0.8213	-0.4472	1.9270	0.3200	1.4720	0.7321	0.1965	-0.5562	0.4286	0.4228	-0.2422	0.3616	0.2037	2.0860	-0.2555	0.5918	0.8887	-0.3117	0.5135	-0.3433	-0.5354	0.000	0.6110	-0.6900	-0.9032	-1.9530	-1.0860	0.5970	-0.1524	-0.0229	0.2492	-0.6676	0.3189	0.1820	-0.7000	0.5953	0.1322	0.6899	0.7517	0.0876	0.2031	-0.1255	-0.7797	-0.6975	-1.3020	-1.6580	0.4863	0.4491	0.5171	1.1830	0.9923	0.3266	0.4194	0.8868	-1.4860	1.2990	-0.1067	0.4731	0.8102	-0.8902	0.4152	0.7180	0.4685	0.4159	0.4962	1.6980	-1.2510	0.8184	0.5395	0.9608	0.6522	1.0130	0.8000	0.7991	-0.2816	-1.0860	-0.0284	0.8201	-2.3680	0.4463	1.0170	-0.3384	0.3203	-0.1423	1.1410	-0.5832	1.4560	1.3370	1.1900	0.2825	0.9525	0.7450	1.8450	1.2100	0.1424	0.2174	1.1460	1.0850	-0.0769	1.8430	0.5867	0.7081	1.6330	0.3024	0.7488	1.0530	0.9515	1.7820	0.3062	0.9136	0.8130	1.2460	1.0620	0.9617	-0.8018	0.5978	1.1730	1.1700	0.5037	1.4360	1.1440	1.0610	0.5983	0.9244	1.8580	0.7240	1.0230	0.6785	0.9988	1.1830	0.8493	1.6500	0.8508	0.7939	1.3550	-0.1573	0.1150	1.8570	1.1400	0.6298	1.6920	1.2770	0.8715	0.3682	-0.1066	1.3230	1.6130	0.3780	0.7722	1.0030	0.8204	1.4730	0.9822	0.6615	1.5350	0.0709	1.2450	0.5045	0.8293	0.9382	0.8332	1.5210	0.1510	0.9571	1.1180	1.6660	0.8384	0.7266	1.5300	0.9714	1.2060	0.7724	0.2943	0.7638	0.3578	0.9178	1.2150	0.8355	-0.3401	1.0140	1.6970	0.3108	1.5660	1.2480	0.8806	0.5137
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34	-0.0167	-0.0785	-1.1100	1.2700	2.0100	0.1524	0.3487	0.6105	0.4083	0.3084	1.6160	0.2098	-0.3479	0.1517	-0.2863	-1.3670	-0.1420	-0.0747	0.0288	-0.3817	0.6677	-0.8622	0.0589	0.2052	0.2843	0.1960	-0.1723	-0.0094	0.0231	-0.5102	-0.0018	0.5771	-0.1687	-0.4525	-0.3032	0.5433	0.5927	-0.0512	-0.8470	0.2156	1.0630	-0.2245	0.5253	-0.5170	0.000	-0.1965	-1.0680	-0.2973	-0.5200	0.2264	0.4647	0.3363	0.6199	0.1542	1.7880	0.4526	-2.6270	-0.5155	-0.0556	-0.1440	-0.1486	1.1070	-0.3817	1.1100	0.3319	0.5590	0.6888	-0.2692	0.6977	0.0466	-0.6661	0.4879	-1.2650	1.2940	0.7243	0.3932	-0.2043	0.1711	-0.1116	0.6981	0.4644	-1.1410	-0.0227	0.8666	0.8108	-3.2370	0.8407	-0.2742	0.1459	-0.2241	0.2587	-0.3695	-2.1220	0.3045	-0.2024	-0.0303	-0.0430	-0.6944	-0.4577	0.0369	0.7699	0.3758	-0.5850	0.7535	0.8358	-0.1055	0.4905	-0.1030	0.1196	-1.6170	-0.0117	-2.0050	-0.6313	-0.6257	-0.8305	-0.4509	-1.5840	-1.0530	-0.9024	-0.7616	-0.3420	-0.2764	-1.3540	-0.2048	-0.4347	-0.6524	-0.9162	-1.7620	-0.2915	-0.2674	-0.2477	-0.3254	0.0408	-1.0020	-0.5920	-0.7003	-0.0350	-1.9760	-0.8947	-0.5589	-0.8430	-1.1360	-1.5970	-0.2157	-0.9672	-0.4501	-0.8109	-0.3100	-0.4794	-1.1580	-0.6701	-0.8097	-0.9587	-0.2609	-0.7314	-1.7680	-0.1748	-1.4680	-0.6644	0.0714	-1.3880	-0.6461	-1.7370	-0.4773	-1.5320	-0.1515	-0.9758	0.0768	-0.7020	-0.7682	-1.6220	-1.4510	-0.2776	-0.4738	-0.1204	-0.3229	-1.4370	-0.8725	-1.1840	-0.1416	-1.5700	-0.7219	-1.3190	-0.2902	-0.2518	0.4167	-0.0306	-0.6578	-0.7490	-0.5614	-0.2924	-1.7060	-1.5380	-0.6887	-0.8990	-0.0437	-0.8838	-1.2060	-1.4340	-0.5755	-0.9548	-0.5346	-0.5270	-0.9161	-0.6379	-1.1830	-1.4460	-0.8897	-1.6710
4667	id_322b2f678	trt_cp	72	D1	-0.5006	-0.1347	-0.4483	-0.8398	-0.2346	0.1326	1.7800	0.0170	-0.2086	-0.3378	-0.3360	2.6540	0.6797	0.2034	-0.4944	0.4033	0.2620	-0.1200	0.2236	-0.5272	0.4911	-0.0150	-0.5422	-0.8465	0.1379	-0.3170	-0.2446	-0.2338	-0.4869	0.7988	-0.0578	0.3167	-0.2543	0.4323	0.9481	-0.5242	0.6324	0.9581	0.3787	1.1340	-1.3190	0.8497	-1.1510	-0.4977	2.0440	-0.9467	0.0034	-0.0100	-0.5730	-0.0570	0.7250	-0.2241	0.2307	-0.2414	0.9825	-0.5967	-4.9690	0.5756	-0.2757	0.7018	-0.5804	0.0321	-0.5989	-0.1055	-0.7435	-0.2836	-0.1038	-0.6388	-0.0786	0.2229	0.5630	0.9295	-0.9174	0.3661	1.2320	-0.1194	-0.0483	-0.2047	-0.6365	1.8650	-0.8296	0.5406	0.1219	0.0767	0.5558	-0.5231	1.0350	0.4428	-0.5968	0.0843	-0.3328	0.9988	-1.6860	-0.8073	-0.3244	0.8442	0.4732	0.0576	-0.7264	0.9572	-0.1287	-1.2810	0.4324	0.7858	0.0000	-0.4350	-0.1647	-1.2320	1.1150	-0.3649	0.1981	-0.7842	0.1203	-1.3090	-0.0473	2.2640	0.3589	1.0500	0.0157	-0.5757	0.2470	-0.7976	0.6536	0.9337	0.4158	-0.4552	0.3954	-0.2682	-0.8613	-0.3167	-0.6033	0.7648	0.0000	0.4050	1.1740	-0.0340	-1.3620	1.2980	-0.1202	-0.0937	-0.1922	-2.8000	-0.1216	0.6726	0.4555	-0.4939	1.0990	-0.3825	0.1834	-0.4664	-0.6139	-0.6465	-0.9963	-0.4681	0.3357	0.5618	0.1952	-0.2167	-1.0550	-0.6171	-0.0970	0.6385	0.7740	0.5024	0.4769	-0.2724	-0.5876	-0.3067	-0.5591	0.6293	0.1978	-1.0750	-0.8929	-0.4768	-0.3856	0.2392	-1.0940	-0.0386	-0.2339	-1.0800	-0.5175	-0.4966	-0.2546	0.1460	0.7699	0.2920	0.5098	0.7797	0.2151	-0.7650	0.6791	0.0383	-0.4098	-1.4500	-2.0110	-0.5084	-1.1740	0.5308	0.4309	-0.6864	1.0730	0.5124	0.0746	0.4991	1.1710	1.1070	0.1016	0.3936	-0.5851	-0.5546	0.7883	-1.0890	-1.1510	0.7395	0.1705	0.6129	-0.0076	-0.6811	-0.4987	-0.3056	-0.0741	-0.1302	-0.7672	-0.1521	0.0141	0.2129	-1.0690	-0.3271	-0.8139	0.2837	1.3200	-0.6794	0.1412	0.1257	0.4780	-1.2790	0.5060	0.2490	0.3372	0.7857	0.7794	0.1522	-0.5630	1.5250	0.9685	-0.0203	-1.1760	0.5135	-1.0030	1.2850	1.1930	1.3060	1.2670	0.9205	-0.1415	0.8225	-0.0834	0.2984	-0.3264	1.3500	-0.2318	-0.4832	-0.3492	0.1625	0.9632	-0.2691	0.6642	-1.3190	-1.0880	0.1511	0.9712	0.3101	0.8055	0.9419	0.1397	-0.6568	-0.3180	-0.3497	-0.0766	0.8865	0.6337	0.0261	-0.0848	0.4611	-0.2493	0.5418	-0.3017	1.2160	0.9631	0.7187	-0.8888	-0.6798	0.7068	0.1433	-0.0902	-1.0410	-0.3128	0.1376	-0.5689	0.3248	0.0825	-0.8824	0.8446	-0.0424	-0.3905	4.1110	-0.2271	0.0307	-0.0580	0.0243	0.4933	-0.4713	0.2587	-0.3638	-0.3919	-0.8153	-0.4694	-0.4800	-0.7330	0.9449	1.5540	0.9055	-0.3618	-0.2785	-0.5215	-0.0911	0.2983	-0.3198	0.1974	0.2588	0.8569	0.1963	1.2280	-0.7224	1.3160	0.5022	-0.6061	0.0274	1.6570	-0.8996	1.2510	-0.6040	-0.8128	-0.0513	0.1486	0.1451	-0.2596	-0.2147	1.0100	0.0651	0.8357	1.1890	-0.4430	-0.5097	-0.1194	-0.6858	-1.2080	-0.6073	0.3891	0.8850	0.0994	0.0155	-0.1157	0.1865	-0.1400	-0.8727	-0.9945	-0.5307	0.2624	0.2130	-0.0307	0.0388	0.1840	2.0060	-0.1400	-1.4200	-0.0565	0.0168	0.4483	-0.1987	0.1035	0.2568	-0.4097	-0.2296	-0.8068	0.7943	-0.4954	0.2909	0.6828	0.2686	0.7666	0.8028	0.3001	0.1880	-0.8192	-1.3650	-0.5618	-0.4630	0.0184	-0.1335	1.0900	-1.1750	0.6422	-0.4123	-0.5968	-0.2459	0.1242	1.2380	-0.7219	0.7568	-0.2701	-1.4370	0.0413	0.9373	0.3888	-1.0820	-0.0688	0.3049	0.0266	0.2598	-0.7312	-0.9319	-0.3197	-0.7517	1.6550	-0.2930	0.3399	-0.4484	-0.2149	0.1352	1.0260	0.7274	0.2769	-0.2521	0.3894	-0.3110	-1.0170	-0.6774	-0.4822	0.4629	-0.6702	0.0046	-0.4336	0.4467	-1.0660	-0.6402	0.1196	-0.8598	0.2584	-0.0464	-0.4771	0.1716	0.3672	-0.4379	-0.4259	-0.9419	-1.1320	-1.0800	0.8220	0.8249	-0.7517	0.0833	2.2330	-0.5920	0.5536	-0.1451	-0.0617	-0.5466	-1.8310	-0.2947	-1.7520	0.0406	-0.8543	0.4111	-0.3103	0.0976	-0.0619	-0.0316	-0.6906	-0.3474	-1.3450	0.0239	-0.4055	0.1057	1.8120	-0.5685	-0.2555	-0.8235	-0.4469	0.2773	0.1695	0.4430	0.9405	-0.0836	0.7395	-0.3911	-0.0517	-0.1343	0.2140	-0.4971	0.9801	0.9452	0.8233	-0.3699	0.2285	1.1250	0.8824	0.8386	0.4734	-0.8251	0.8608	-0.0578	-0.6173	-1.2380	0.1701	1.0510	-0.2526	1.2040	-0.3329	0.7273	-1.6240	1.1390	-0.0206	1.8940	0.2728	-0.2611	-0.9583	-0.1533	0.8695	-0.0658	-0.3670	0.4135	-0.7684	0.3644	1.0110	0.3519	-0.0867	-0.0620	0.2814	0.3110	3.4250	1.0420	-0.1232	0.8452	-0.9925	-0.5925	-0.5367	-0.2750	-1.1060	-0.4908	-0.0917	0.5969	0.5711	-0.5812	-0.4448	-0.4588	0.6708	0.6775	0.1809	-0.5785	-0.4013	-0.0628	-0.4025	-0.0726	0.4482	-0.8611	-0.0669	-0.3444	0.2336	-0.2384	-0.8140	0.5990	-0.0661	-0.1201	0.9277	0.9349	0.7737	0.9028	-0.1288	-0.0197	0.8805	-0.4588	0.1198	0.7918	0.3216	-0.0943	-0.3554	-0.9947	-1.0870	-1.2890	0.7711	-0.1264	-0.7388	0.1914	0.8870	-0.3788	1.2420	-0.1994	0.7279	0.2133	1.6600	-1.1050	0.1528	-0.4394	0.6898	-0.3925	-0.8342	-0.0421	0.1072	1.5690	0.6472	0.4025	-0.9387	0.0401	0.2032	0.4356	-0.5707	1.0040	0.2755	0.4593	-0.2916	1.0080	0.4656	-0.3796	-0.3221	0.4043	0.1713	0.0474	0.3843	0.3371	-0.9088	-0.1015	-0.9146	0.1604	-0.0719	0.6469	-0.5145	0.4711	0.6489	0.2927	-0.6391	-1.1220	0.8542	-0.6673	1.2740	-0.0633	1.0400	0.7184	0.8992	-0.4990	1.3780	0.1855	-0.6936	-4.6780	0.0040	0.0273	-0.6950	0.7352	0.9968	1.0660	-0.1331	0.2907	0.6671	0.2998	0.6875	-1.8520	0.2957	0.3617	-0.4825	-0.2939	0.7351	1.0490	-0.7646	0.1014	-2.4800	-1.1280	-0.0630	-1.0860	-0.6377	1.2660	-1.0530	0.2513	0.2822	-1.1960	-0.0579	-1.4560	1.2150	1.6210	-0.4081	0.7673	0.6794	-1.5920	-1.5430	-0.9510	-0.0769	-0.5796	0.5672	0.3668	0.0712	0.6670	0.2398	-0.4824	-0.0097	-0.6874	-0.8872	0.2376	0.6223	0.000	-0.6843	0.0781	0.1903	0.4035	0.6470	-0.5074	0.9620	-1.9400	-0.4288	-1.4860	0.2563	-0.4442	1.0150	-0.2332	-2.0130	0.0367	-0.4706	0.5379	-1.0340	0.2799	-0.5290	-0.2482	0.5541	0.0860	-0.0423	-0.9638	-0.1592	0.5518	-0.7861	-0.8890	0.1731	0.0583	-0.6696	-0.0318	0.7700	-0.6164	2.2560	-0.5505	-0.0935	0.3985	-4.9840	0.7120	0.5205	0.8600	0.8424	0.1486	-0.5679	0.0143	-0.2222	-0.2031	1.1090	0.3246	-0.8573	0.4846	0.0748	-0.9209	-0.5432	0.2609	-0.6064	0.3342	1.0950	0.4944	-0.2804	1.0120	0.0311	0.2105	-0.2224	0.1980	0.2818	0.2967	0.4452	-0.8932	0.3471	-0.0399	0.6467	0.8726	-0.1983	-0.0998	0.9376	0.2089	0.2471	-0.5016	0.5253	0.2605	-0.5106	0.3879	-0.1256	0.0217	0.3525	0.6330	-0.4262	0.8410	-0.3086	0.2837	-0.6467	-0.0643	-0.1532	0.5589	0.1874	-0.3255	-0.5315	0.1064	-0.1527	0.3532	0.2105	0.3615	0.4655	0.4108	0.0268	0.8855	-0.0753	-0.3167	0.8598	0.9451	0.3878	0.7249	1.1420	-0.0686	0.8748	0.0814	-1.2410	0.2664	-0.8967	-0.4452	-0.1887	-0.4504	0.2436	0.7658	0.2651	0.4942	-0.2967	0.5357	0.2072	1.4010	0.3974	0.1654	0.6244	-0.0579	-0.2815	0.1217	0.4632	0.6141	0.0263	0.1398	0.0072	0.0324	0.2174	0.9139	-0.5058	0.4386	-1.1340	-0.3925	-0.0159	-0.4141	0.2484	-0.6096	0.7482	-0.3195	0.0733	0.4909	0.3914	0.9686	-0.0612	-0.2119
15548	id_a76c49729	trt_cp	48	D2	-0.6777	0.1007	-0.8098	0.1861	-0.2613	-0.9035	-0.8875	-0.0357	0.0289	0.9500	0.6200	1.7160	0.3618	-0.8997	0.5295	0.1386	1.3470	0.0836	-0.2017	0.2268	0.0781	-0.4554	0.2506	-0.1511	-0.3879	-0.1274	0.0640	0.2610	0.1160	0.1285	-0.2883	-0.5915	-0.8396	0.4176	0.7406	-0.1207	0.5123	0.0954	-0.7838	0.1265	0.0059	0.1202	-0.8921	-0.8277	0.6155	0.3599	-0.8942	0.5878	-0.1530	0.4997	-0.0705	0.4944	0.3303	0.3261	-1.1110	-1.1170	-1.8380	0.6840	-0.9062	-0.3928	-0.0678	-0.3981	0.1046	0.3546	-0.5144	0.3752	0.1383	-0.8788	0.9769	-0.2956	0.1109	0.4949	3.1090	-0.4977	0.2175	-0.2769	0.0341	-0.4814	-0.3677	0.3135	-0.9860	0.6344	0.1593	-0.0671	0.4664	-1.8180	0.1365	-0.2524	-0.3841	0.5265	0.6537	-2.7610	-0.9109	-0.2170	0.2440	-0.2478	0.7872	0.0573	0.1693	-0.1706	-0.7400	-0.1025	-0.4100	-0.5989	-1.6690	1.6490	-0.4068	-0.9353	-1.2490	0.2225	-0.3056	0.0000	0.0929	-0.2215	0.2483	0.7026	-0.0803	-0.1085	1.2060	-0.3589	0.0433	0.4887	0.1915	1.8230	-0.1040	0.6292	-0.3082	-0.5463	0.0031	-0.8982	-0.1358	0.0327	0.1137	-0.8350	0.1998	-0.6060	-0.1871	0.9601	1.1160	-0.4491	0.2559	0.6998	-0.3687	-0.2148	0.1535	-0.9216	0.2829	-0.7849	0.0083	-0.6876	0.3317	0.0703	0.8513	0.0000	-0.2499	0.5527	0.7593	0.0550	-0.4599	-1.1950	1.5990	0.3827	-0.1669	1.5320	0.1009	-0.5822	0.1388	0.2123	0.8128	0.5626	0.5897	-0.0694	-0.3747	0.0429	-0.3093	-0.0019	-0.5022	0.6770	-0.1262	0.0530	-0.1494	0.1766	-0.5381	-0.4140	0.6100	-0.7905	-0.2128	0.0227	-0.6863	-0.1790	0.3152	-0.3229	0.2093	0.3383	0.1599	1.1370	-0.7136	-0.4972	0.2344	0.3740	-0.0379	-0.1118	-0.1672	0.7736	0.0418	-1.3940	0.2259	0.2678	0.4622	-0.0457	-0.6078	-0.0762	-0.3259	0.4128	0.3693	0.2819	0.7541	0.2979	0.2568	1.2330	0.0132	-0.2453	-0.8987	-1.5690	-0.2298	0.3550	-0.6744	0.0026	0.2599	0.1018	0.2372	0.1705	-0.6444	0.4093	0.1732	-0.1895	-0.0217	-0.5293	-0.3089	-1.0100	-0.2868	-0.2822	-0.0222	-0.2213	-0.0938	-0.1335	-0.8913	0.3319	-0.5130	0.0659	0.6486	0.4295	0.3831	-0.0510	-0.7932	0.6381	0.3881	-0.2242	-0.1928	0.1695	-0.3279	0.4926	0.1589	0.7026	-0.1052	-0.9507	0.0964	0.8178	-0.0403	0.0055	-0.4202	-0.0953	-0.0242	0.1202	-0.0110	0.4825	-0.7163	-0.2448	0.0445	-0.0321	-0.1960	0.3827	0.9312	0.4794	0.9736	-0.5267	1.0320	-0.5241	0.5678	0.3900	-0.5654	-0.2323	0.3745	-0.5175	0.0865	1.8400	0.0488	0.7629	-0.3270	-0.3959	0.4128	0.3862	-0.4353	0.4850	-0.6019	-0.1032	0.8984	0.0000	0.8248	-0.1539	-0.8734	0.5807	-0.1703	0.2014	-1.1750	-0.0461	0.4521	0.2512	-0.0138	-0.1758	-0.2919	-0.3688	0.9496	-0.1597	-0.4374	0.8399	0.3392	1.0980	0.2209	-0.0299	-0.3701	0.8478	0.8065	0.4513	-0.0621	0.2868	0.4529	0.4916	0.8130	0.2117	-0.2973	0.0881	0.5422	0.0196	-0.1875	-0.6213	0.1726	-0.2043	-0.0798	0.0649	0.2030	-0.1387	-0.5264	0.8607	-0.3982	-0.2362	-0.2381	-0.4018	0.1616	-0.1749	0.0668	0.1558	0.1722	-0.5784	0.3223	0.4691	-0.1504	0.2964	-0.0856	0.0853	-1.0330	-0.8210	-0.2929	-0.0063	-0.4708	-0.2089	1.2070	-0.4239	0.2126	1.0340	-0.7115	-0.1978	0.2881	0.2678	0.1641	0.2748	0.1454	-0.3758	-0.5390	-0.6127	1.5770	0.9479	-0.2578	0.1173	0.2522	-0.2419	-0.5590	0.4456	-0.5597	0.5245	-0.0160	0.1047	0.1090	-0.2086	-0.6653	0.5227	0.3368	0.1269	0.0640	0.2789	0.1709	0.4596	-0.0730	-0.0283	0.2229	-0.6138	-1.0210	-0.4973	0.1861	0.4033	0.1096	-0.7638	-0.2530	0.4189	-0.7951	-0.1312	0.5038	-0.2290	-0.3101	-0.2289	-0.0311	0.4742	0.1570	0.3522	0.2087	-0.3160	0.0226	-0.4995	0.0910	0.2587	-0.0562	-0.0406	0.4534	0.1309	-0.5292	0.5038	0.2124	0.1354	-0.0934	-0.2068	-0.0062	-0.1835	-0.7330	0.0583	-0.2617	-0.2803	-0.2700	1.1990	-0.2231	-0.1416	-0.8511	-0.1942	1.0310	0.4636	-0.2681	-1.2570	0.5221	-0.7299	0.2321	-0.3548	-0.3299	0.7606	0.8262	0.0081	0.0007	-0.3436	0.2070	-0.7163	0.5196	0.0883	-0.7507	0.2742	-0.1403	-0.7932	-0.4902	-0.2678	0.5659	0.4780	-0.0644	0.2042	0.5304	-0.1289	-0.3494	0.6064	-0.3056	0.2855	-0.7645	0.1363	0.1113	0.6211	0.3837	1.3050	-0.2975	0.1839	-0.2901	1.1620	0.0194	0.0529	-0.2388	0.2840	-0.3876	0.5818	-0.0249	0.2403	-0.5595	0.9491	-0.7504	0.6988	0.3211	-0.2048	-0.2092	0.4460	-0.0469	0.0087	0.0907	0.6083	-1.4760	-0.3274	-0.0434	-0.3350	0.0522	0.1208	0.1329	-0.0674	-0.8123	-0.5036	-2.0260	-0.4506	-0.2891	0.1102	-0.3076	-0.0914	-0.1599	-0.6305	0.1411	0.3701	0.7299	0.6320	-0.1083	-0.3760	-0.2357	0.1273	-1.3550	-0.1011	0.0708	-2.0320	-1.0610	0.8866	-0.1356	-0.5335	-0.1840	0.4604	0.3045	0.2431	0.1869	0.2731	0.0994	0.1490	-1.4130	0.1011	0.2378	-0.1132	1.0450	-0.6993	0.7664	-0.1106	-0.5020	0.6047	-0.2169	0.2975	-0.2321	-0.4918	-3.1650	-0.3292	0.3260	0.0349	0.4601	0.0944	-0.5447	-1.2610	0.7451	0.0664	1.3980	-0.1933	0.0471	0.8399	0.2553	0.5130	0.0566	0.4976	-0.0175	-0.5318	0.6814	0.2054	0.5571	0.7109	0.6919	-0.2591	3.3140	0.2026	-1.0070	-0.7517	-0.5234	-0.2185	-0.1207	-0.5525	0.6535	-0.4255	0.0000	0.1541	0.4140	0.4733	0.5373	0.0799	-0.0828	-0.6363	0.0179	-0.0813	0.0435	0.3978	-0.1447	0.3455	0.4833	-0.3903	-0.1605	-0.1433	0.3442	-0.6513	-0.8893	-0.5210	-0.3230	0.9554	-0.0833	-0.6177	0.4015	0.0003	-0.2696	0.1759	-0.0532	-0.0644	0.0714	-1.1670	0.1626	-0.2720	0.9636	0.9492	0.6354	-0.1807	-0.1377	-0.2658	0.2471	0.4267	-1.1370	-0.6187	-0.0336	0.5342	-0.3624	0.5574	0.0432	0.3936	0.1103	-0.0432	0.4080	0.7206	0.3311	-0.1390	-0.5357	-0.5272	0.2273	0.4337	0.1639	-0.1984	1.8730	-0.3170	-0.3524	0.1797	0.1612	-0.3976	0.0778	-0.4232	-0.2360	-0.3656	-2.0730	-0.3312	1.6890	0.3839	0.0037	0.5552	0.2512	-0.5646	0.5940	0.5822	0.6957	0.0721	-0.9810	0.2173	-0.7615	6.752	0.7180	-0.2263	-0.1181	0.1996	-0.1409	-0.7820	1.5100	-1.2780	0.3983	0.6707	-1.1390	-0.0385	0.0066	-0.3718	0.0222	0.5375	0.0249	-0.5788	0.8846	-0.5273	0.1624	-0.1577	-0.0449	-0.2919	-0.5358	-0.8864	-0.0057	-0.4924	-0.5581	0.4100	0.3404	0.4277	-0.3076	0.3575	0.3062	0.2327	0.4783	0.0379	-0.6813	0.5035	0.2477	0.0566	0.8579	0.3025	-0.6503	-0.1153	0.1204	-0.5326	-0.4915	0.3009	-0.0000	0.1772	0.2970	-0.0095	-0.1539	0.5410	-0.3909	-0.0307	0.0278	0.2865	0.5412	1.2760	0.0059	0.0099	0.3556	-0.1529	1.5140	0.3974	0.3087	0.1148	0.7931	1.1210	0.8686	0.1537	0.8961	0.6515	0.8348	0.6309	0.0654	0.7857	0.2343	0.4501	-0.5436	0.9379	-0.1203	-0.1641	-0.4581	0.4573	0.1958	0.4810	0.6693	-0.5356	0.4397	-0.4328	0.8612	-0.4027	-0.1639	0.4039	0.6812	0.1047	0.9511	0.9203	-0.1856	-0.1825	1.1480	-0.2244	0.0647	0.2521	1.0260	0.3180	0.2386	0.6636	0.8791	-0.2125	0.8770	0.1849	1.0800	0.6961	0.0500	0.3468	-0.5960	-0.4146	0.5995	-0.4157	0.0839	-0.2670	0.7369	0.6765	1.5610	1.0560	-0.2675	0.1420	1.1920	0.0536	0.1485	0.8645	0.9234	0.7668	-0.6377	0.6745	0.2707	0.6061	0.9026	0.4502	-0.1404	0.4445	-0.2716	0.2585	0.8593	0.9947	0.6548	1.1500	0.5236	0.3692	0.5623	0.1486	-0.2745	0.2116	0.3791	-0.0103	0.2047	-0.2459	0.0227	0.6276
15206	id_a35ce02ff	trt_cp	48	D1	-0.1526	0.2129	0.3058	-1.0440	2.2800	-0.1027	-0.7309	-1.0210	0.7081	0.0221	-0.2360	0.2567	0.3017	0.4678	-0.3926	0.8063	0.1127	0.4733	0.2705	-2.5530	0.7114	1.2270	0.0396	-0.1004	0.1981	0.6113	0.6585	1.0580	0.0172	-0.1055	0.7925	-0.4698	-0.5843	-0.3670	0.0971	0.5068	0.0465	0.6080	-0.6118	0.6293	-1.1460	1.1540	1.0390	0.2246	1.6560	0.0592	0.1544	-0.3616	0.0876	-0.7121	0.4346	0.2782	0.0545	-0.0382	0.0870	0.1007	0.9892	-0.5497	-0.7938	0.7749	-1.2060	0.2508	1.1090	0.0369	1.5350	0.5649	-1.2990	0.8453	0.7973	-0.2449	0.2900	-0.1010	-0.4523	-0.1310	-0.9074	0.2610	0.1959	0.5540	-0.1603	-0.2330	0.5750	-0.5827	0.1420	-0.7251	-0.9433	-0.3981	-0.1516	0.1196	0.0569	0.3461	0.5634	-0.2366	-0.4779	-0.3626	0.6679	-1.1630	0.6611	0.6468	0.0301	0.1334	-0.0118	0.2048	-0.9810	0.1189	0.5339	-0.3964	-0.0620	0.4798	-0.1098	0.4712	-0.3707	0.0314	0.5057	0.3075	-1.0390	-0.9381	-0.4653	-0.3410	0.4666	0.2732	0.7373	0.6851	-0.4792	-0.9913	-1.7500	-0.1761	-0.4259	0.0877	0.5653	0.4220	0.0116	-1.1960	-0.0253	-0.3965	-1.1450	-0.6659	0.1727	-0.0858	0.9570	0.0650	-0.2865	0.1669	-0.8250	0.6579	-0.3831	-0.9277	-0.6599	-0.9126	-0.5149	-0.7720	0.3823	0.4313	0.2053	-1.1450	-0.2344	-0.2597	0.6728	0.0721	-0.0916	1.0950	2.6840	0.0973	0.2327	-0.2538	-0.1958	0.1650	0.7278	-0.2338	-0.1138	0.7426	0.0756	1.3600	-1.4070	-1.0720	-1.0980	-0.2229	-0.7472	0.2499	-0.5925	1.0800	-0.5791	0.3410	-0.2281	0.8849	0.8084	0.9745	-0.6433	0.6560	-0.2838	0.8336	0.2401	-0.2753	-0.2732	-0.4063	0.5319	0.3769	0.4461	-0.1592	0.1246	0.9251	-0.5194	-0.4414	-0.6696	-0.2119	-4.5490	0.4042	-0.3105	0.5443	0.5003	-0.7303	-0.3140	0.8234	0.3662	-0.2368	1.0210	0.6179	0.4944	0.4368	1.0050	-0.0829	0.3512	-0.8505	-0.2526	-0.5806	-0.0602	-0.1200	0.6182	-0.2265	-0.1477	-0.5577	0.7386	-0.1867	0.7491	0.1055	-0.1713	0.1610	-0.2790	-0.5401	-0.2648	0.5015	-0.2630	0.3998	1.0650	-0.5479	-0.1868	-0.4488	-0.8163	1.7060	0.4311	-1.6970	0.3560	-0.1182	-0.1610	-0.4450	0.3964	0.2540	0.8641	0.6969	-1.1310	-0.3835	0.0905	-0.5865	1.1250	0.2790	0.2007	0.5659	-0.2888	-0.8916	-0.6604	0.3032	-0.4484	1.2490	-0.6665	0.0673	0.2564	0.7041	0.2259	0.3544	-0.2162	-0.9560	-0.7073	0.0780	0.4211	-0.0766	-1.1320	-0.6167	0.9319	0.3726	0.3464	-0.7113	1.3850	1.5630	0.2022	-0.5690	0.4327	-1.2050	0.6157	1.7130	0.5024	-0.2192	0.5331	-0.3557	1.3070	-0.3465	-0.4042	-0.4793	0.3138	0.0000	0.5729	0.3271	0.2814	0.1040	-0.3914	-0.4082	0.8693	0.3049	0.6378	-0.2216	0.4029	0.3554	-0.1473	-0.9961	-0.1026	0.7509	-0.5277	0.0073	0.0092	-0.5754	-0.6215	-0.6379	-0.1996	0.1588	0.0377	0.0761	-0.0829	-0.7544	-0.6036	-0.7867	0.2327	0.5969	-0.4283	-0.3214	0.5285	0.5419	-0.6890	0.5411	0.2743	-0.7085	-0.3848	0.4209	0.4461	1.2130	0.3853	0.2569	-0.2607	-0.6338	-1.2260	0.3123	-0.3873	-0.6759	0.1612	0.7159	-1.3700	-0.0889	0.9550	-0.2690	-0.3293	-1.3740	-0.0226	0.4033	-0.8121	0.1796	0.1922	-0.7140	-1.4730	-0.0390	0.6217	-0.1103	0.2408	0.3141	0.1901	-0.0692	0.4530	-0.3414	0.1452	0.7577	0.2916	0.0637	-0.4586	0.1593	0.2462	2.5910	-1.0750	0.0948	0.4489	0.7179	0.8099	0.7171	0.2933	0.3994	1.0050	-0.1451	0.4312	0.3528	0.2929	-0.6356	0.4081	0.2007	-0.6139	-0.6158	-0.0004	-0.0946	-0.3164	2.4910	-0.6001	-0.7964	0.9273	-1.0870	-0.7087	1.1280	0.2938	-0.8679	0.3765	-0.2705	-0.0040	-0.1057	0.0789	0.1394	-0.3640	1.7340	1.3920	0.6887	-0.2208	0.1037	0.0798	0.4772	-0.6018	-1.1810	0.4972	0.1830	0.9052	-0.5649	-0.9062	-0.6119	0.0824	0.6000	0.4108	0.5348	-0.6345	-0.6156	1.6280	-0.1754	-1.0100	0.6917	-0.2996	0.0625	0.3949	0.4985	-1.2350	-0.0054	-0.1163	-0.2265	-0.0917	0.0046	-0.6546	-0.6740	0.9547	-0.4166	0.1440	-0.7065	0.2646	-0.0715	0.2212	0.2849	0.5445	-1.2250	-0.4635	0.3113	-1.3550	-0.2907	-0.0208	-0.2657	-0.1560	1.1100	-0.9872	0.0062	0.2908	-1.3330	-1.1290	0.3764	-0.5469	0.0389	-1.2930	-0.4945	0.2375	0.3549	-0.1391	-0.1304	-1.1360	-0.1967	0.3898	-0.0022	-1.1080	0.5662	-0.0115	-0.5003	-1.3540	-1.6290	0.0070	-0.4597	1.1440	0.6612	-1.1860	-0.1768	1.9210	-0.0229	0.3514	-0.2533	0.6725	-0.2445	0.6274	-0.2643	1.3450	-0.0066	0.8712	0.8656	-0.9218	0.5897	-0.8773	-0.3931	0.2083	-0.1674	0.6696	-0.3368	0.5681	0.1890	-0.1302	-2.7890	-2.2050	-0.3122	0.4063	-0.0496	0.9761	-0.6073	0.3205	0.4795	0.6959	-0.3562	0.3426	-0.1735	-0.4499	-0.8435	1.0080	0.0382	1.2870	0.3834	-0.6310	-0.5716	0.6141	-1.0820	1.0060	0.8451	-0.1910	0.5756	0.0322	0.6938	0.1772	-0.2593	0.5596	0.4822	0.0387	0.6676	0.1358	0.0924	0.1634	-0.6880	-0.1491	0.3531	-0.4473	-0.4076	-0.3976	0.7254	-0.6898	-0.3272	-0.3216	-0.1905	0.8643	-0.0647	-0.4750	-0.6180	0.5951	0.0645	2.0940	-0.1825	-1.0730	-0.0415	-1.3720	-0.6603	-0.3233	0.3050	-1.1390	1.2200	1.5160	-0.0985	1.2000	0.2752	-0.5358	-0.5309	1.8520	-0.3647	-0.3025	0.0435	-0.1277	0.4393	-0.3551	0.4311	0.2374	-1.6760	-0.0127	-0.4813	0.1516	0.8809	-0.0335	0.2144	0.0120	-0.2362	0.3346	-0.8321	-0.7824	-0.9354	1.3510	-0.5102	0.6022	-0.2070	-1.2430	-0.8522	-0.2362	-0.1878	0.6928	-0.2050	-0.2764	-0.0449	-0.7709	0.3076	0.4719	-0.6644	-0.3058	0.9704	-1.1360	-1.7200	-0.5073	-0.2969	-0.5966	0.2007	-0.4141	0.0454	-0.0506	0.1224	0.2501	-0.9097	1.1740	-0.1664	-0.0415	0.3121	0.4919	-0.4932	-1.2200	0.4236	0.4210	0.3084	0.7734	-0.4959	-0.2708	-0.1698	1.0990	0.2618	0.1292	1.2460	0.8731	0.0484	1.1100	-0.3223	-0.4765	-1.2510	0.4771	-0.2285	-0.4168	0.4510	-0.0005	-1.1210	-0.7268	-0.4287	1.1920	-0.9382	-0.1155	0.2838	-0.2900	0.4224	-0.2552	0.7934	0.9278	-0.3272	-0.5549	1.4460	0.6289	0.9173	-0.3803	0.000	-0.5710	-0.0351	-0.6073	-2.6980	-0.3112	1.7530	0.9880	0.0013	-0.3908	1.8450	-0.1763	0.2720	-0.1730	-0.1729	0.4730	0.0034	-0.5938	0.4693	0.2134	0.0366	-0.4857	0.4248	0.4918	0.0506	-0.4457	-0.3843	1.0340	-0.0560	-1.0820	0.2756	-0.0932	0.0984	0.0153	-0.7045	0.5213	-0.2555	-1.3490	-0.5163	-0.3776	-0.2739	0.1658	0.4221	-0.7849	-1.3110	-0.6288	0.3956	0.0146	1.1690	-0.3541	0.1572	-0.6960	0.0546	-1.5200	-0.3443	-1.3600	-0.8691	-0.6392	0.4014	0.7926	0.8280	0.4010	0.0406	-0.9950	0.0686	1.4800	0.5726	0.8928	-0.7204	1.1440	0.5811	1.2730	0.6219	0.9699	-0.0349	1.0730	0.4477	0.4943	0.4052	1.1940	1.2830	-0.1543	1.7070	0.4439	0.3052	0.2597	0.1742	-0.3020	0.7376	0.4184	0.7977	0.5319	-0.1446	-0.2625	0.2412	1.5200	0.8398	0.4680	0.2487	1.2200	-0.3369	0.0465	0.4458	0.9195	0.6617	0.3676	0.6587	0.8850	0.7863	0.2350	0.2233	0.7874	1.2630	1.1460	0.8645	1.4760	0.2711	0.6063	0.3185	1.1620	-0.5644	0.3426	0.7003	0.8884	0.5506	0.5042	-0.1441	0.2776	0.9069	0.1023	0.2273	0.2053	0.6758	0.5511	0.5327	0.3419	0.5058	0.6916	0.9818	0.1522	0.1786	-0.1822	0.8305	0.2073	0.8627	1.1570	1.2660	0.3842	1.0200	-0.1927	0.4562	0.1754	0.4041	1.2890	-0.3114	0.7094	1.2400	0.2386	0.0022	1.0430	-0.8604	0.2769	0.0707	1.1430	0.9360

print('Number of rows in training set:', train_features.shape[0])
print('Number of columns in training set:', train_features.shape[1] - 1)
print('Number of rows in test set:', test_features.shape[0])
print('Number of columns in test set:', test_features.shape[1] - 1)

Number of rows in training set: 19051
Number of columns in training set: 875
Number of rows in test set: 4763
Number of columns in test set: 875

train_features['dataset'] = 'train'
test_features['dataset'] = 'test'
df = pd.concat([train_features, test_features])
ds = df[df['dataset']=='train']
ds = ds.groupby(['cp_type', 'cp_time', 'cp_dose'])['sig_id'].count().reset_index()

ds.columns = [
    'cp_type', 
    'cp_time', 
    'cp_dose', 
    'count'
]

fig = px.sunburst(
    ds, 
    path=[
        'cp_type',
        'cp_time',
        'cp_dose' 
    ], 
    values='count', 
    title='Sunburst chart for all cp_type/cp_time/cp_dose',
    width=500,
    height=500
)

fig.show()

train_columns = train_features.columns.to_list()

g_list = [i for i in train_columns if i.startswith('g-')]

c_list = [i for i in train_columns if i.startswith('c-')]

# correlation visulization between features (randomly selected 50)

columns = g_list + c_list
for_correlation = np.random.choice(columns, 50)
data = df[for_correlation]
f = plt.figure(
    figsize=(18, 18)
)

plt.matshow(
    data.corr(), 
    fignum=f.number
)

plt.xticks(
    range(data.shape[1]), 
    data.columns, 
    fontsize=14, 
    rotation=50
)

plt.yticks(
    range(data.shape[1]), 
    data.columns, 
    fontsize=14
)

cb = plt.colorbar()
cb.ax.tick_params(
    labelsize=13
)

This indicates there are high correlation between features. Now try to find pairs of features with high correlation.

# columns of integer values
cols = ['cp_time'] + columns
all_columns = list()
for i in range(0, len(cols)):
    for j in range(i+1, len(cols)):
        if abs(train_features[cols[i]].corr(train_features[cols[j]])) > 0.9:
            all_columns = all_columns + [cols[i], cols[j]]
            
# set the columns to make sure each appear one time
all_columns = list(set(all_columns))
print('Number of columns:', len(all_columns))

Number of columns: 41

In total we have 35 columns that have correlation with at least another 1 higher than 0.9.

Now let's check target and do some basic analysis on the feature-target relationship.

train_target = pd.read_csv("train_targets_scored.csv")

print('Number of rows: ', train_target.shape[0])
print('Number of cols: ', train_target.shape[1])

train_target.head()

Number of rows:  23814
Number of cols:  207

	sig_id	5-alpha_reductase_inhibitor	11-beta-hsd1_inhibitor	acat_inhibitor	acetylcholine_receptor_agonist	acetylcholine_receptor_antagonist	acetylcholinesterase_inhibitor	adenosine_receptor_agonist	adenosine_receptor_antagonist	adenylyl_cyclase_activator	adrenergic_receptor_agonist	adrenergic_receptor_antagonist	akt_inhibitor	aldehyde_dehydrogenase_inhibitor	alk_inhibitor	ampk_activator	analgesic	androgen_receptor_agonist	androgen_receptor_antagonist	anesthetic_-_local	angiogenesis_inhibitor	angiotensin_receptor_antagonist	anti-inflammatory	antiarrhythmic	antibiotic	anticonvulsant	antifungal	antihistamine	antimalarial	antioxidant	antiprotozoal	antiviral	apoptosis_stimulant	aromatase_inhibitor	atm_kinase_inhibitor	atp-sensitive_potassium_channel_antagonist	atp_synthase_inhibitor	atpase_inhibitor	atr_kinase_inhibitor	aurora_kinase_inhibitor	autotaxin_inhibitor	bacterial_30s_ribosomal_subunit_inhibitor	bacterial_50s_ribosomal_subunit_inhibitor	bacterial_antifolate	bacterial_cell_wall_synthesis_inhibitor	bacterial_dna_gyrase_inhibitor	bacterial_dna_inhibitor	bacterial_membrane_integrity_inhibitor	bcl_inhibitor	bcr-abl_inhibitor	benzodiazepine_receptor_agonist	beta_amyloid_inhibitor	bromodomain_inhibitor	btk_inhibitor	calcineurin_inhibitor	calcium_channel_blocker	cannabinoid_receptor_agonist	cannabinoid_receptor_antagonist	carbonic_anhydrase_inhibitor	casein_kinase_inhibitor	caspase_activator	catechol_o_methyltransferase_inhibitor	cc_chemokine_receptor_antagonist	cck_receptor_antagonist	cdk_inhibitor	chelating_agent	chk_inhibitor	chloride_channel_blocker	cholesterol_inhibitor	cholinergic_receptor_antagonist	coagulation_factor_inhibitor	corticosteroid_agonist	cyclooxygenase_inhibitor	cytochrome_p450_inhibitor	dihydrofolate_reductase_inhibitor	dipeptidyl_peptidase_inhibitor	diuretic	dna_alkylating_agent	dna_inhibitor	dopamine_receptor_agonist	dopamine_receptor_antagonist	egfr_inhibitor	elastase_inhibitor	erbb2_inhibitor	estrogen_receptor_agonist	estrogen_receptor_antagonist	faah_inhibitor	farnesyltransferase_inhibitor	fatty_acid_receptor_agonist	fgfr_inhibitor	flt3_inhibitor	focal_adhesion_kinase_inhibitor	free_radical_scavenger	fungal_squalene_epoxidase_inhibitor	gaba_receptor_agonist	gaba_receptor_antagonist	gamma_secretase_inhibitor	glucocorticoid_receptor_agonist	glutamate_inhibitor	glutamate_receptor_agonist	glutamate_receptor_antagonist	gonadotropin_receptor_agonist	gsk_inhibitor	hcv_inhibitor	hdac_inhibitor	histamine_receptor_agonist	histamine_receptor_antagonist	histone_lysine_demethylase_inhibitor	histone_lysine_methyltransferase_inhibitor	hiv_inhibitor	hmgcr_inhibitor	hsp_inhibitor	igf-1_inhibitor	ikk_inhibitor	imidazoline_receptor_agonist	immunosuppressant	insulin_secretagogue	insulin_sensitizer	integrin_inhibitor	jak_inhibitor	kit_inhibitor	laxative	leukotriene_inhibitor	leukotriene_receptor_antagonist	lipase_inhibitor	lipoxygenase_inhibitor	lxr_agonist	mdm_inhibitor	mek_inhibitor	membrane_integrity_inhibitor	mineralocorticoid_receptor_antagonist	monoacylglycerol_lipase_inhibitor	monoamine_oxidase_inhibitor	monopolar_spindle_1_kinase_inhibitor	mtor_inhibitor	mucolytic_agent	neuropeptide_receptor_antagonist	nfkb_inhibitor	nicotinic_receptor_agonist	nitric_oxide_donor	nitric_oxide_production_inhibitor	nitric_oxide_synthase_inhibitor	norepinephrine_reuptake_inhibitor	nrf2_activator	opioid_receptor_agonist	opioid_receptor_antagonist	orexin_receptor_antagonist	p38_mapk_inhibitor	p-glycoprotein_inhibitor	parp_inhibitor	pdgfr_inhibitor	pdk_inhibitor	phosphodiesterase_inhibitor	phospholipase_inhibitor	pi3k_inhibitor	pkc_inhibitor	potassium_channel_activator	potassium_channel_antagonist	ppar_receptor_agonist	ppar_receptor_antagonist	progesterone_receptor_agonist	progesterone_receptor_antagonist	prostaglandin_inhibitor	prostanoid_receptor_antagonist	proteasome_inhibitor	protein_kinase_inhibitor	protein_phosphatase_inhibitor	protein_synthesis_inhibitor	protein_tyrosine_kinase_inhibitor	radiopaque_medium	raf_inhibitor	ras_gtpase_inhibitor	retinoid_receptor_agonist	retinoid_receptor_antagonist	rho_associated_kinase_inhibitor	ribonucleoside_reductase_inhibitor	rna_polymerase_inhibitor	serotonin_receptor_agonist	serotonin_receptor_antagonist	serotonin_reuptake_inhibitor	sigma_receptor_agonist	sigma_receptor_antagonist	smoothened_receptor_antagonist	sodium_channel_inhibitor	sphingosine_receptor_agonist	src_inhibitor	steroid	syk_inhibitor	tachykinin_antagonist	tgf-beta_receptor_inhibitor	thrombin_inhibitor	thymidylate_synthase_inhibitor	tlr_agonist	tlr_antagonist	tnf_inhibitor	topoisomerase_inhibitor	transient_receptor_potential_channel_antagonist	tropomyosin_receptor_kinase_inhibitor	trpv_agonist	trpv_antagonist	tubulin_inhibitor	tyrosine_kinase_inhibitor	ubiquitin_specific_protease_inhibitor	vegfr_inhibitor	vitamin_b	vitamin_d_receptor_agonist	wnt_inhibitor
0	id_000644bb2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	id_000779bfc	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	id_000a6266a	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	id_0015fd391	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	id_001626bd3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

x = train_target.drop(['sig_id'], axis=1).sum(axis=0).sort_values().reset_index()

x.columns = [
    'column', 
    'nonzero_records'
]

x = x.tail(50)

fig = px.bar(
    x, 
    x='nonzero_records', 
    y='column', 
    orientation='h', 
    title='Columns with the higher number of positive samples (top 50)'
)

fig.show()

x = train_target.drop(['sig_id'], axis=1).sum(axis=0).sort_values(ascending=False).reset_index()

x.columns = [
    'column', 
    'count'
]

x['count'] = x['count'] * 100 / len(train_target)

fig = px.bar(
    x, 
    x='column', 
    y='count', 
    orientation='v', 
    title='Percent of positive records for every column in target'
)

fig.show()

The biggest number of positive samples for 1 target column is 3.5%. So we deal here with highly imbalanced data.

data = train_target.drop(['sig_id'], axis=1).astype(bool).sum(axis=1).reset_index()

data.columns = [
    'row', 
    'count'
]

data = data.groupby(['count'])['row'].count().reset_index()

fig = px.bar(
    data, 
    y=data['row'], 
    x="count", 
    title='Number of activations in targets for every sample'
)

fig.show()

data = train_target.drop(['sig_id'], axis=1).astype(bool).sum(axis=1).reset_index()

data.columns = [
    'row', 
    'count'
]

data = data.groupby(['count'])['row'].count().reset_index()

fig = px.pie(
    data, 
    values=100 * data['row'] / len(train_target), 
    names="count", 
    title='Number of activations in targets for every sample (Percent)'
)

fig.show()

We can see here that about 40% of sample have zeros in all columns and more than 50% have only one active target column.

After looking at the feature and target data repectively, it's time to find the most correlated features(gene differential expression or cell expression variaty) for every target column (every single checkpoint/label in MOA).

correlation_matrix = pd.DataFrame()

for t_col in train_target.columns:
    # create a list for each checkpoint/label 
    corr_list = list()
    if t_col == 'sig_id':
        continue
    for col in columns:
        res = train_features[col].corr(train_target[t_col])
        corr_list.append(res)
    correlation_matrix[t_col] = corr_list

correlation_matrix['train_features'] = columns
correlation_matrix = correlation_matrix.set_index('train_features')

correlation_matrix

	5-alpha_reductase_inhibitor	11-beta-hsd1_inhibitor	acat_inhibitor	acetylcholine_receptor_agonist	acetylcholine_receptor_antagonist	acetylcholinesterase_inhibitor	adenosine_receptor_agonist	adenosine_receptor_antagonist	adenylyl_cyclase_activator	adrenergic_receptor_agonist	adrenergic_receptor_antagonist	akt_inhibitor	aldehyde_dehydrogenase_inhibitor	alk_inhibitor	ampk_activator	analgesic	androgen_receptor_agonist	androgen_receptor_antagonist	anesthetic_-_local	angiogenesis_inhibitor	angiotensin_receptor_antagonist	anti-inflammatory	antiarrhythmic	antibiotic	anticonvulsant	antifungal	antihistamine	antimalarial	antioxidant	antiprotozoal	antiviral	apoptosis_stimulant	aromatase_inhibitor	atm_kinase_inhibitor	atp-sensitive_potassium_channel_antagonist	atp_synthase_inhibitor	atpase_inhibitor	atr_kinase_inhibitor	aurora_kinase_inhibitor	autotaxin_inhibitor	bacterial_30s_ribosomal_subunit_inhibitor	bacterial_50s_ribosomal_subunit_inhibitor	bacterial_antifolate	bacterial_cell_wall_synthesis_inhibitor	bacterial_dna_gyrase_inhibitor	bacterial_dna_inhibitor	bacterial_membrane_integrity_inhibitor	bcl_inhibitor	bcr-abl_inhibitor	benzodiazepine_receptor_agonist	beta_amyloid_inhibitor	bromodomain_inhibitor	btk_inhibitor	calcineurin_inhibitor	calcium_channel_blocker	cannabinoid_receptor_agonist	cannabinoid_receptor_antagonist	carbonic_anhydrase_inhibitor	casein_kinase_inhibitor	caspase_activator	catechol_o_methyltransferase_inhibitor	cc_chemokine_receptor_antagonist	cck_receptor_antagonist	cdk_inhibitor	chelating_agent	chk_inhibitor	chloride_channel_blocker	cholesterol_inhibitor	cholinergic_receptor_antagonist	coagulation_factor_inhibitor	corticosteroid_agonist	cyclooxygenase_inhibitor	cytochrome_p450_inhibitor	dihydrofolate_reductase_inhibitor	dipeptidyl_peptidase_inhibitor	diuretic	dna_alkylating_agent	dna_inhibitor	dopamine_receptor_agonist	dopamine_receptor_antagonist	egfr_inhibitor	elastase_inhibitor	erbb2_inhibitor	estrogen_receptor_agonist	estrogen_receptor_antagonist	faah_inhibitor	farnesyltransferase_inhibitor	fatty_acid_receptor_agonist	fgfr_inhibitor	flt3_inhibitor	focal_adhesion_kinase_inhibitor	free_radical_scavenger	fungal_squalene_epoxidase_inhibitor	gaba_receptor_agonist	gaba_receptor_antagonist	gamma_secretase_inhibitor	glucocorticoid_receptor_agonist	glutamate_inhibitor	glutamate_receptor_agonist	glutamate_receptor_antagonist	gonadotropin_receptor_agonist	gsk_inhibitor	hcv_inhibitor	hdac_inhibitor	histamine_receptor_agonist	histamine_receptor_antagonist	histone_lysine_demethylase_inhibitor	histone_lysine_methyltransferase_inhibitor	hiv_inhibitor	hmgcr_inhibitor	hsp_inhibitor	igf-1_inhibitor	ikk_inhibitor	imidazoline_receptor_agonist	immunosuppressant	insulin_secretagogue	insulin_sensitizer	integrin_inhibitor	jak_inhibitor	kit_inhibitor	laxative	leukotriene_inhibitor	leukotriene_receptor_antagonist	lipase_inhibitor	lipoxygenase_inhibitor	lxr_agonist	mdm_inhibitor	mek_inhibitor	membrane_integrity_inhibitor	mineralocorticoid_receptor_antagonist	monoacylglycerol_lipase_inhibitor	monoamine_oxidase_inhibitor	monopolar_spindle_1_kinase_inhibitor	mtor_inhibitor	mucolytic_agent	neuropeptide_receptor_antagonist	nfkb_inhibitor	nicotinic_receptor_agonist	nitric_oxide_donor	nitric_oxide_production_inhibitor	nitric_oxide_synthase_inhibitor	norepinephrine_reuptake_inhibitor	nrf2_activator	opioid_receptor_agonist	opioid_receptor_antagonist	orexin_receptor_antagonist	p38_mapk_inhibitor	p-glycoprotein_inhibitor	parp_inhibitor	pdgfr_inhibitor	pdk_inhibitor	phosphodiesterase_inhibitor	phospholipase_inhibitor	pi3k_inhibitor	pkc_inhibitor	potassium_channel_activator	potassium_channel_antagonist	ppar_receptor_agonist	ppar_receptor_antagonist	progesterone_receptor_agonist	progesterone_receptor_antagonist	prostaglandin_inhibitor	prostanoid_receptor_antagonist	proteasome_inhibitor	protein_kinase_inhibitor	protein_phosphatase_inhibitor	protein_synthesis_inhibitor	protein_tyrosine_kinase_inhibitor	radiopaque_medium	raf_inhibitor	ras_gtpase_inhibitor	retinoid_receptor_agonist	retinoid_receptor_antagonist	rho_associated_kinase_inhibitor	ribonucleoside_reductase_inhibitor	rna_polymerase_inhibitor	serotonin_receptor_agonist	serotonin_receptor_antagonist	serotonin_reuptake_inhibitor	sigma_receptor_agonist	sigma_receptor_antagonist	smoothened_receptor_antagonist	sodium_channel_inhibitor	sphingosine_receptor_agonist	src_inhibitor	steroid	syk_inhibitor	tachykinin_antagonist	tgf-beta_receptor_inhibitor	thrombin_inhibitor	thymidylate_synthase_inhibitor	tlr_agonist	tlr_antagonist	tnf_inhibitor	topoisomerase_inhibitor	transient_receptor_potential_channel_antagonist	tropomyosin_receptor_kinase_inhibitor	trpv_agonist	trpv_antagonist	tubulin_inhibitor	tyrosine_kinase_inhibitor	ubiquitin_specific_protease_inhibitor	vegfr_inhibitor	vitamin_b	vitamin_d_receptor_agonist	wnt_inhibitor
train_features
g-0	-0.006007	-0.011043	0.002654	-0.017350	-0.032366	-0.010064	-0.019147	-0.013574	-0.008087	-0.027887	-0.028450	-0.007635	-0.002248	0.022710	0.003916	-0.014771	-0.017003	-0.011592	-0.011624	-0.008619	-0.013938	-0.009755	-0.007138	0.014179	-0.003685	-0.015397	-0.009751	-0.002329	-0.016863	-0.010908	-0.010899	0.012580	-0.014953	-0.000619	0.002627	0.029332	0.010438	0.009469	-0.007565	-0.007299	-0.000104	-0.001933	-0.014768	-0.020042	-0.012677	-0.015739	0.070041	0.022153	0.009043	-0.016706	-0.007592	-0.001175	0.001298	0.000864	-0.011517	-0.011199	-0.017088	-0.008848	-0.014467	-0.003632	-0.002006	-0.015280	-0.008177	0.069552	-0.009738	0.031492	-0.010838	-0.006375	-0.007103	-0.001916	-0.009177	-0.026379	-0.009223	0.010863	-0.004465	-0.003759	0.007684	-0.009292	-0.016815	-0.025961	0.105539	0.000458	NaN	-0.023860	-0.002249	-0.002917	-0.000197	-0.009565	0.025593	0.014150	-0.006568	-0.010430	-0.007093	0.002963	-0.015354	-0.008336	-0.020138	-0.005130	-0.012129	-0.030768	-0.018000	0.003261	-0.000688	0.038110	-0.013065	-0.022240	-0.005991	-0.005533	-0.009801	-0.005052	0.031495	-0.006387	0.025131	-0.009119	0.002200	0.000154	-0.010400	-0.003786	0.018694	0.005750	0.008027	-0.005902	-0.001114	-0.000832	0.002351	0.000453	-0.003652	0.009228	-0.009705	-0.005198	-0.002806	-0.017863	-0.005976	-0.008115	-0.007974	-0.008845	0.574599	-0.005016	-0.006772	0.037484	-0.008445	-0.007915	0.048826	-0.013460	-0.012855	-0.013734	-0.005367	-0.016962	-0.016623	0.014554	0.001512	-0.021281	-0.012194	0.005372	-0.012844	-0.012448	-0.007653	-0.010691	-0.005986	-0.015290	0.001128	-0.005639	-0.016742	0.619957	-0.001470	-0.004304	0.045249	0.001659	-0.009570	0.039064	-0.002224	0.005266	-0.007314	0.000109	-0.001574	0.026736	-0.018111	-0.029857	-0.006001	-0.006902	-0.008482	-0.001496	-0.014999	-0.003266	-0.001578	0.000948	-0.004067	-0.017122	-0.009536	-0.012718	-0.002118	-0.010934	-0.004526	0.001958	0.079017	-0.005711	-0.006101	-0.004017	-0.011398	-0.036784	-0.012742	-0.006444	0.013732	-0.005891	-0.005379	-0.007795
g-1	-0.004971	-0.005058	0.003298	-0.006640	0.006940	0.011542	-0.006746	0.005025	-0.007046	0.014378	0.003500	-0.005268	0.003408	-0.001269	-0.008160	-0.008424	0.009997	0.010475	0.013808	0.011711	-0.015517	-0.001389	-0.002025	-0.000144	0.004147	0.007917	-0.008611	0.011958	-0.007803	0.000559	-0.000117	0.013821	0.015191	-0.010107	0.004277	0.008432	0.016728	-0.003612	0.009251	0.001591	0.010757	0.011051	0.001474	-0.013690	-0.006266	-0.011465	-0.006251	0.016896	-0.009972	0.009511	0.003239	0.022246	-0.001477	0.009963	-0.006635	0.008016	0.006686	0.005226	0.004458	0.010457	-0.004068	-0.005339	-0.008499	-0.035594	-0.000111	0.020628	-0.003312	0.005408	0.010254	-0.000693	0.005829	0.005892	0.000838	0.000022	0.007991	-0.001402	0.007339	-0.007694	0.004613	0.009936	-0.020410	0.005017	NaN	-0.001771	-0.008088	-0.000365	-0.002608	0.005022	0.009691	-0.021895	0.003580	0.004127	-0.003136	0.008988	0.012486	0.000983	-0.006766	-0.011890	-0.001170	0.014126	-0.008451	0.010943	-0.009464	-0.024481	0.013083	0.005482	-0.005410	-0.001865	0.003679	-0.027312	0.037303	-0.003506	0.011857	-0.004981	-0.011946	0.011079	-0.004480	0.007774	0.002587	-0.021667	-0.003286	-0.001172	0.003546	0.004495	0.004776	-0.004912	-0.000829	-0.009825	0.000063	0.015330	0.002768	0.007142	0.000234	-0.067842	0.015357	-0.004983	0.072434	0.004073	0.008165	0.016325	0.003151	0.004329	0.017236	-0.000322	0.017278	-0.001560	-0.002461	-0.003473	-0.011243	-0.022096	0.008975	0.004201	0.014081	-0.031484	-0.003193	-0.000809	0.010278	-0.008250	-0.006132	0.005865	-0.016964	-0.002998	-0.007200	0.074115	0.005556	-0.002053	0.020235	0.004990	0.002287	-0.022345	0.001324	0.007632	-0.007499	0.000841	-0.003980	0.005209	0.008294	0.006419	0.002524	-0.001208	0.005823	-0.004382	0.006761	0.003074	-0.007617	-0.004366	-0.018137	0.010511	-0.001601	-0.011751	0.003421	-0.001214	-0.004821	0.024805	-0.010071	0.004387	-0.006176	0.009056	0.004859	-0.003588	0.008386	-0.003083	0.001079	0.001452	0.000272	0.003958
g-2	-0.001984	-0.002700	-0.002011	-0.002725	-0.018601	-0.002516	-0.003492	-0.016661	0.005478	-0.007902	-0.009866	0.000723	0.014898	-0.005380	0.004487	0.002279	0.006405	-0.008586	0.002340	-0.000717	0.004462	-0.000696	0.005656	0.004367	0.003903	0.000458	0.003210	0.009704	-0.018881	-0.012397	0.005049	0.020139	0.001241	0.007200	0.003864	0.000694	0.001253	-0.013411	-0.010020	-0.001256	-0.007652	-0.007250	0.005467	-0.001662	0.002890	-0.012318	0.006732	-0.008365	0.005849	0.008738	0.000941	-0.016310	-0.011463	-0.000503	-0.010653	-0.006854	-0.001443	-0.002291	0.007369	0.021730	0.003636	-0.004767	-0.003055	0.011779	0.003084	0.005818	0.011438	0.002209	0.005201	0.001756	0.004897	-0.002123	-0.014008	0.001553	-0.004498	-0.017394	0.001250	0.010283	-0.000852	0.002197	0.012135	-0.005339	NaN	-0.003528	0.000071	-0.003731	-0.004115	0.014173	-0.007361	-0.017285	-0.001401	0.000592	0.007120	0.004944	-0.000934	-0.013478	-0.011491	0.006265	0.003215	-0.003509	-0.014592	-0.003834	-0.010676	-0.025808	-0.007289	-0.019047	0.011242	0.010451	0.012852	-0.019581	0.047109	0.008958	0.001954	-0.013559	-0.010781	-0.002994	-0.004646	0.006841	-0.011446	-0.012246	0.015277	0.004341	0.010868	-0.000864	-0.005239	-0.005052	-0.000410	-0.002996	-0.003500	-0.009756	0.001323	-0.008469	0.000135	0.034078	-0.006795	0.001093	0.160079	-0.005429	0.005677	0.000574	0.001872	0.001265	-0.015179	-0.013231	-0.001864	-0.006688	-0.009745	0.006145	-0.002569	-0.009652	-0.006332	0.002083	-0.001181	0.023541	-0.004827	0.006440	0.006445	0.001690	-0.003007	-0.009205	-0.013829	0.003328	-0.001071	0.172686	-0.002804	-0.001614	-0.003026	-0.000128	0.009480	-0.012333	-0.007580	0.003469	0.000121	-0.013257	0.011351	0.015291	-0.009259	0.000517	0.007498	-0.017686	-0.009550	-0.004268	0.004671	-0.000375	0.005715	0.014923	-0.003185	0.007105	-0.024546	0.006135	0.004169	-0.004312	0.010261	-0.006626	0.020673	-0.001101	0.006890	0.004934	0.006331	0.002272	0.014082	0.003939	-0.000294	-0.007587	0.015116	0.002855
g-3	-0.010818	-0.000298	-0.001878	-0.008248	-0.010287	-0.013344	-0.002372	-0.003356	0.007711	-0.013194	-0.009678	-0.007104	-0.005937	-0.008192	-0.008471	0.007490	-0.003519	-0.014394	-0.005415	-0.006420	0.006974	-0.008795	-0.004658	-0.013735	-0.000176	0.000030	-0.002300	-0.002080	-0.007759	-0.007506	-0.002052	-0.011903	-0.009633	-0.004618	0.005548	-0.011010	-0.002641	0.003343	0.025518	0.002367	-0.010244	0.004571	-0.013080	-0.016188	-0.015562	-0.010223	-0.003134	-0.008017	0.017224	0.005265	-0.003857	0.061434	0.002349	-0.012313	-0.007181	-0.001459	-0.008541	-0.000872	-0.003974	-0.001826	0.000525	-0.001807	-0.005524	0.193813	-0.012970	-0.000482	-0.010481	-0.000973	-0.008992	-0.004211	-0.005966	-0.002658	0.002688	0.004372	-0.005252	-0.008482	-0.009826	-0.014928	-0.010154	-0.015121	-0.029975	0.004020	NaN	-0.008076	-0.012697	-0.005579	0.005269	0.003606	0.002212	0.026593	0.017760	-0.016396	-0.006448	-0.005431	-0.003794	-0.008273	0.009166	0.003009	-0.003544	-0.007322	0.007169	0.007534	0.007863	0.072007	-0.014416	-0.004965	-0.000070	-0.017794	-0.011798	0.010577	0.158018	-0.002351	-0.011631	0.000508	-0.009611	-0.005850	0.024517	-0.007695	0.007817	0.012486	-0.009634	-0.002920	0.002864	-0.003173	-0.009777	-0.013019	-0.007648	0.023308	-0.003733	0.002902	-0.003271	-0.011199	0.002670	-0.070563	-0.007277	-0.003807	0.137788	0.022886	-0.007851	-0.015845	-0.010426	-0.004168	-0.013618	-0.008482	-0.013866	-0.010702	0.005889	0.002256	0.000907	0.019580	0.001187	-0.008585	-0.007786	-0.043752	0.010441	-0.004011	-0.011222	0.020039	0.005396	-0.007969	0.002452	-0.000679	-0.000549	0.151840	-0.003062	0.008740	-0.021862	-0.006335	-0.010107	0.064489	0.000439	-0.003800	-0.005840	0.007660	0.015429	-0.005410	0.002517	-0.022740	-0.003997	-0.001001	0.000128	0.005002	-0.020003	0.009392	-0.000699	-0.001398	0.013206	-0.012025	0.011021	-0.000785	0.002729	-0.003951	-0.001604	-0.005745	0.053504	0.001953	0.016259	-0.012067	-0.016760	-0.005062	-0.003646	-0.000998	-0.000430	-0.001705	-0.006844	0.001685
g-4	0.001699	0.000146	0.006671	-0.009327	-0.006801	-0.010815	-0.008187	-0.000052	-0.004351	-0.008954	-0.005163	0.004995	-0.003318	0.006970	-0.003411	-0.019432	-0.008528	-0.003048	0.009659	-0.002498	0.004149	-0.002201	-0.007368	-0.009038	-0.001514	-0.006419	-0.001715	-0.000729	-0.007977	-0.001116	-0.000140	0.001775	0.001044	-0.000614	0.012755	0.001114	-0.008779	-0.001515	-0.003892	0.002187	-0.003707	-0.006285	-0.007292	-0.016191	0.001486	-0.010081	0.012164	0.004426	-0.005580	0.000994	-0.001040	0.015280	-0.008510	-0.004028	-0.013234	-0.002173	-0.009890	-0.003224	-0.008533	-0.007502	0.000712	-0.008632	-0.005578	0.060074	-0.008421	-0.000977	0.000716	-0.004029	0.007298	-0.003881	-0.002241	-0.016652	-0.007272	-0.006649	-0.004053	-0.001146	0.002280	-0.001847	-0.009193	-0.009004	-0.046754	0.015765	NaN	0.000442	0.005514	0.001587	-0.003488	-0.008473	-0.004485	0.000124	-0.005167	-0.002415	0.001468	-0.004130	-0.005689	-0.013148	-0.001638	-0.001274	0.000867	-0.005679	-0.000150	0.002944	-0.006885	0.013226	-0.007116	-0.004235	-0.008615	-0.000634	-0.006514	0.014467	-0.000652	0.000183	-0.011701	0.002696	-0.008640	-0.009952	-0.011411	-0.001419	0.008047	-0.006798	0.001058	-0.003776	-0.002126	-0.000649	-0.000948	0.015583	0.002112	0.010900	-0.009876	-0.001074	-0.001449	0.005300	-0.002089	-0.021603	-0.001119	0.004271	0.363048	0.010109	-0.003584	0.004797	-0.000567	0.002181	0.002857	-0.001924	0.000523	-0.003970	-0.018145	0.006562	-0.001501	-0.001125	-0.003030	0.002565	0.001426	-0.001734	-0.010221	-0.010585	-0.001710	-0.011697	-0.008776	0.005405	-0.011685	-0.007102	-0.004238	0.390954	0.001784	-0.003742	-0.027427	-0.010344	0.000707	0.006592	0.004893	0.001119	-0.005877	-0.008468	-0.005792	0.027096	-0.008664	-0.001131	-0.004939	0.006875	-0.003782	-0.010466	0.007105	0.016357	-0.007511	-0.005295	0.004000	-0.005440	-0.028898	-0.008914	0.003039	0.003910	-0.006549	-0.001476	0.066927	-0.000752	-0.000366	0.003050	-0.006600	-0.023082	0.000257	0.000043	-0.002629	0.002383	-0.002530	0.004698
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
c-95	0.007517	0.003339	0.003284	0.020767	0.029922	0.015643	0.018362	0.014709	0.001939	0.023496	0.030032	-0.012176	0.002605	-0.009362	0.004756	0.002653	0.013545	0.015001	0.016980	0.013004	0.009107	0.023183	0.001831	0.000639	-0.002612	0.004694	0.007195	0.007758	0.014076	0.009715	0.008698	-0.005249	0.015991	0.003680	-0.001712	-0.011943	-0.025736	-0.007942	-0.015679	0.005845	0.009083	0.008705	0.004887	0.019727	0.012898	0.011695	-0.015187	-0.030151	-0.003207	0.011295	0.003704	0.014979	0.009474	0.004534	0.028688	0.008258	0.012205	0.008745	0.007061	0.005795	0.001401	0.020428	0.004144	0.012750	0.009753	-0.046756	0.013603	0.010959	0.010885	0.001875	0.013148	0.037323	0.013748	-0.003808	0.010107	0.003406	0.008803	0.001055	0.016955	0.036460	0.016398	0.002706	NaN	0.015969	0.003414	0.006582	-0.017739	0.008880	0.002875	0.016722	0.018291	0.008251	0.002104	0.015537	0.016428	0.011253	0.042221	0.004609	0.015704	0.030778	0.009630	0.008099	0.010521	-0.061829	0.015458	0.027804	0.006616	0.006865	0.014757	0.014611	-0.100470	-0.006057	-0.011542	0.015840	0.012022	0.007228	0.017159	0.010270	0.004574	0.023251	-0.000201	-0.000149	0.001636	0.004306	0.005517	0.005713	0.000224	-0.020727	0.014593	0.011352	0.001606	0.013537	0.002146	-0.079288	0.014823	0.013139	-0.615680	0.004513	0.003133	-0.028003	0.007812	0.007879	-0.025512	0.013697	0.009036	0.003931	0.006331	0.001338	-0.000579	0.014771	0.003697	0.029902	0.011538	-0.050752	0.009317	0.009278	0.011308	0.009605	0.007736	0.019385	0.005572	0.003154	0.013812	-0.665852	0.007350	-0.009954	-0.034001	0.005898	0.011467	0.003229	0.007055	-0.009922	-0.000188	0.004673	-0.018746	-0.006657	0.021650	0.031417	0.016552	0.007916	0.009110	0.009811	0.017650	0.017251	-0.008711	0.003987	-0.010308	0.008060	0.004281	0.007970	0.003389	0.009305	0.003191	0.011628	-0.110170	0.008537	0.005534	-0.004121	0.008528	-0.060795	-0.004988	0.004407	-0.015039	0.012133	0.010144	0.009759
c-96	0.008114	0.008086	0.011352	0.018879	0.030464	0.013364	0.012477	0.015054	0.007089	0.028466	0.031752	-0.001818	0.003153	-0.007917	0.009530	0.002171	0.013228	0.021132	0.012685	0.010743	0.009894	0.022972	0.001858	0.002194	0.000321	0.007238	0.007243	0.007831	0.013789	0.011340	0.011955	0.001832	0.012844	0.004998	0.004159	-0.005610	-0.040935	-0.010177	-0.019420	0.004621	0.010653	0.011687	0.009474	0.025132	0.015795	0.016320	0.000291	-0.009132	-0.010266	0.014723	0.005307	0.025392	0.009117	0.006592	0.028267	0.008906	0.012350	0.010057	0.015060	0.005785	0.000248	0.020317	0.003768	-0.022021	0.010472	-0.042301	0.012071	0.010580	0.016454	0.002187	0.018524	0.032382	0.018462	-0.008901	0.006647	0.002918	0.003974	0.007041	0.016274	0.035619	-0.025385	0.003515	NaN	0.017134	0.004766	0.006927	-0.006842	0.004802	-0.003606	0.015465	-0.005079	0.007259	0.008104	0.015692	0.023054	0.012012	0.044542	0.006971	0.016093	0.034342	0.005367	0.012283	0.012372	-0.016073	0.013469	0.030053	0.016925	0.007441	0.015639	0.035389	-0.038122	-0.012121	-0.004134	0.012558	0.008524	0.006027	0.017305	0.010971	-0.009906	0.028744	0.003050	0.002265	0.011590	0.004303	0.007700	0.002565	0.003557	-0.050182	0.019782	0.005332	0.001753	0.018434	0.000237	-0.084422	0.009458	0.011548	-0.698655	0.003859	0.006998	-0.012686	0.009158	0.006189	-0.017901	0.009890	0.011028	0.009596	0.013930	0.001828	0.008063	0.020887	0.001658	0.025928	0.004975	-0.054005	0.004042	0.011616	0.014128	0.016451	0.014275	0.019631	0.005892	0.007521	0.011919	-0.758840	0.004788	-0.004824	-0.030375	0.008516	0.009239	-0.021859	0.005523	-0.000821	-0.001971	0.011899	-0.024506	-0.013751	0.024110	0.034710	0.012035	0.010260	0.008742	0.006130	0.020502	0.012985	-0.013004	0.008828	-0.005017	0.010186	0.014925	0.008064	0.008077	0.011255	0.002159	0.012754	-0.110952	0.006941	0.002928	-0.002783	0.010353	-0.089357	0.007660	0.002751	0.002217	0.009680	0.014185	0.010345
c-97	0.007314	0.003998	0.009813	0.020665	0.027409	0.016308	0.015043	0.013543	-0.005129	0.029761	0.033465	-0.006606	-0.000836	-0.001754	0.010798	0.004516	0.010127	0.012893	0.016403	0.008586	0.007191	0.016303	-0.000246	-0.002845	0.002457	0.008768	0.002799	0.006018	0.015095	0.013827	0.012942	-0.002358	0.011962	-0.000661	0.000045	-0.015369	-0.023079	-0.005038	-0.004880	0.003439	0.012738	0.012553	0.006807	0.020725	0.011511	0.012601	-0.019034	-0.007961	0.000711	0.011405	0.005722	0.022415	0.012327	0.003003	0.026310	0.010240	0.008608	0.009084	0.010092	0.012786	0.002118	0.012649	0.007405	-0.034060	0.007948	-0.025879	0.008041	0.017164	0.013940	0.000528	0.011724	0.035664	0.018359	-0.005519	0.009092	0.002092	0.011265	0.001716	0.017224	0.034134	0.034929	0.001914	NaN	0.021365	0.000477	0.010493	-0.004378	0.006274	0.006299	0.019704	0.003979	0.006921	0.001747	0.017007	0.023847	0.005007	0.025712	0.001320	0.010296	0.030737	0.011005	0.012233	0.012997	-0.009317	0.013294	0.026658	0.009549	0.010729	0.013095	0.029601	-0.055393	-0.002374	0.000028	0.017052	-0.003502	0.020772	0.018400	0.007883	-0.007839	0.030401	0.003105	0.001723	0.008092	0.009001	0.009484	0.006344	-0.000430	-0.021132	0.019916	0.015822	0.002756	0.014572	0.000901	-0.059955	0.018008	0.011670	-0.682858	0.008621	0.006978	-0.022449	0.008841	0.004522	-0.024380	0.005789	0.013328	0.003579	0.015578	0.008363	0.012911	0.022136	0.006246	0.028789	0.013878	-0.033058	0.005800	0.011308	0.019038	0.015943	0.010827	0.017802	0.003116	0.008854	0.015757	-0.739938	0.007995	-0.004234	-0.023920	0.007192	0.018102	-0.000226	-0.001533	0.003643	0.000474	0.010499	-0.008006	-0.014134	0.024344	0.025117	0.015668	0.011705	0.010599	0.008674	0.020508	0.009411	-0.008624	0.006334	-0.003341	0.009239	0.007072	0.006619	0.001968	0.014722	0.001481	0.007654	-0.092896	0.005478	0.002459	-0.000764	0.011836	-0.118757	0.009115	-0.000067	0.010918	0.008291	0.004069	0.009064
c-98	0.011285	0.009887	0.011527	0.029033	0.036224	0.020604	0.019483	0.012947	0.005887	0.033896	0.040910	-0.022793	-0.002091	-0.003445	0.011992	0.010154	0.015472	0.020886	0.008622	0.013036	0.011668	0.018540	0.004556	-0.011252	0.002278	0.010501	0.008989	0.007058	0.024757	0.013628	0.013523	0.000193	0.015312	0.001957	0.003903	-0.016955	-0.047961	-0.010410	-0.039235	0.006052	0.015013	0.014006	0.016157	0.030377	0.017426	0.023079	-0.045660	-0.018578	-0.031294	0.016710	0.009323	0.003303	0.005700	0.007090	0.028616	0.011136	0.009638	0.012027	0.018297	0.010616	0.003546	0.017399	0.005936	-0.064453	0.012612	-0.037854	0.013116	0.011846	0.015336	0.006933	0.009661	0.040488	0.016586	-0.003808	0.007654	0.002697	0.007447	0.009123	0.021903	0.037373	-0.017150	0.005128	NaN	0.024484	0.004058	0.007430	-0.008224	0.008340	-0.061016	-0.088548	-0.002834	0.010007	0.002573	0.018989	0.024869	0.015489	0.021477	0.012124	0.021716	0.042626	0.014039	0.008608	0.016895	0.019283	0.016652	0.032176	0.007429	0.007477	0.017492	0.037933	-0.109561	-0.011726	-0.001089	0.009351	0.004323	0.012075	0.014809	0.004193	-0.020141	-0.089155	-0.002179	0.005524	0.013935	0.011290	0.006542	0.003729	-0.058108	-0.017648	0.018792	0.010238	0.006768	0.023025	0.000387	-0.095487	0.017801	0.015124	-0.622139	0.004950	0.011183	-0.021054	0.014659	0.004880	-0.019179	0.014191	0.008734	0.008759	0.024454	-0.008142	0.006299	-0.108331	-0.001714	0.035562	0.010375	-0.056397	-0.001773	0.014697	0.020085	0.014148	0.014666	0.017648	0.006913	0.012527	0.014788	-0.672456	0.011744	-0.000123	-0.032054	-0.006070	0.016297	-0.118963	0.003577	0.002375	0.006545	0.005829	-0.017005	-0.006629	0.034002	0.033886	0.010135	0.010404	0.008154	0.010736	0.026731	0.010446	-0.028932	0.010161	-0.008060	0.012666	0.021582	0.010038	0.012850	0.011991	0.004060	0.015232	-0.130699	0.005930	0.003016	-0.000523	0.009669	-0.031209	0.000522	0.003440	-0.081973	0.007996	0.011470	0.015651
c-99	0.003535	0.006934	0.013351	0.020932	0.029841	0.011156	0.011859	0.013246	-0.001052	0.026708	0.024155	-0.023855	-0.005779	-0.002550	0.011287	0.009312	0.019843	0.014350	0.009632	0.010598	0.006868	0.016768	0.001228	-0.011034	0.003299	0.004455	0.009987	0.003903	0.016471	0.010231	0.007082	-0.001457	0.015140	0.001381	0.004070	-0.009799	-0.048903	-0.005929	-0.008365	0.004489	0.010485	0.010057	0.012926	0.028959	0.013977	0.006923	-0.033315	-0.024340	-0.002512	0.008939	0.004870	0.020340	0.006094	0.004532	0.019392	0.005341	0.011663	0.006423	0.011438	0.002589	0.002966	0.009199	0.003142	-0.066441	0.010337	-0.028616	0.016333	0.006774	0.009607	0.000715	0.010383	0.028229	0.020408	-0.003289	0.005521	-0.002440	0.008979	0.012292	0.019111	0.024159	0.023701	0.005751	NaN	0.013126	-0.006031	0.006947	-0.006131	0.009010	0.000281	0.012588	-0.012120	0.009239	-0.001001	0.007991	0.013775	0.008277	0.027328	0.006883	0.015196	0.025224	0.007702	0.010327	0.007524	-0.064563	0.012060	0.017869	0.000933	-0.000286	0.009419	0.022107	-0.034459	-0.005352	-0.007368	0.009602	0.005589	0.008023	0.007528	0.007240	-0.018569	0.026311	0.007918	-0.001533	0.008350	0.006055	0.002281	0.004248	-0.002785	-0.003630	0.021641	0.003704	0.001018	0.016565	0.004116	-0.075121	0.005382	0.009371	-0.584899	-0.001827	0.012278	-0.014004	0.008016	0.003487	-0.019407	0.007898	0.003097	0.006131	0.017311	0.005862	0.010052	0.014157	0.001791	0.015973	0.008189	-0.044531	-0.004918	0.010611	0.016567	0.006116	0.015470	0.012024	0.003829	0.011041	0.016800	-0.633643	0.004546	-0.001532	-0.023724	0.001458	0.007954	0.019549	-0.007533	0.005879	0.000961	0.009205	0.007409	-0.020171	0.020644	0.025122	0.012384	0.005894	0.002176	0.014896	0.015172	0.006261	-0.005816	0.009432	-0.017110	0.003597	0.010319	0.004770	0.010319	0.009187	-0.000584	0.007570	-0.139686	-0.000706	0.005663	-0.005052	0.008486	-0.013741	0.010302	0.000911	0.003492	0.002551	0.008584	0.004071

872 rows × 206 columns

Let's see what is the higher value (absolute) of correlation for target columns with every column from train set. Every column on chart is max correlation of current target column with all of columns from training set.

maxCol=lambda x: max(x.min(), x.max(), key=abs)

high_scores = correlation_matrix.apply(maxCol, axis=0).reset_index()

high_scores.columns = [
    'column', 
    'best_correlation'
]

fig = px.bar(
    high_scores, 
    x='column', 
    y="best_correlation", 
    orientation='v', 
    title='Best correlation with train columns for every target column', 
    width=1200,
    height=800
)

fig.show()

Now let's see what columns from training set have the higher number of "high" correlations with target columns. Every row from chart means that column times has the best value of correlation with different target columns.

col_df = pd.DataFrame()
tr_cols = list()
tar_cols = list()

for col in correlation_matrix.columns:
    tar_cols.append(col)
    tr_cols.append(
        correlation_matrix[col].abs().sort_values(ascending=False).reset_index()['train_features'].head(1).values[0]
    )

col_df['column'] = tar_cols
col_df['train_best_column'] = tr_cols

total_scores = pd.merge(high_scores, col_df)

total_scores

	column	best_correlation	train_best_column
0	5-alpha_reductase_inhibitor	-0.033099	g-591
1	11-beta-hsd1_inhibitor	-0.018874	g-140
2	acat_inhibitor	-0.020002	g-350
3	acetylcholine_receptor_agonist	-0.036279	g-91
4	acetylcholine_receptor_antagonist	0.044712	g-486
...	...	...	...
201	ubiquitin_specific_protease_inhibitor	0.019562	g-18
202	vegfr_inhibitor	-0.081973	c-98
203	vitamin_b	-0.035109	g-356
204	vitamin_d_receptor_agonist	0.055290	g-627
205	wnt_inhibitor	-0.020348	g-252

206 rows × 3 columns

count_features = total_scores['train_best_column'].value_counts().reset_index().sort_values('train_best_column')

count_features.columns = [
    'column', 
    'count'
]

count_features = count_features.tail(33)

fig = px.bar(
    count_features, 
    x='count', 
    y="column", 
    orientation='h', 
    title='Columns from training set with number of high correlations with target columns'
)

fig.show()

target_columns = train_target.columns.tolist()
target_columns.remove('sig_id')
for_analysis = [
    target_columns[
        np.random.randint(0, len(target_columns)-1)
    ] for i in range(5)
]

current_corr = correlation_matrix[for_analysis]

col_df = pd.DataFrame()
tr_first_cols = list()
tr_second_cols = list()
tar_cols = list()

for col in current_corr.columns:
    tar_cols.append(col)
    tr_first_cols.append(
        current_corr[col].abs().sort_values(ascending=False).reset_index()['train_features'].head(2).values[0]
    )
    tr_second_cols.append(
        current_corr[col].abs().sort_values(ascending=False).reset_index()['train_features'].head(2).values[1]
    )

col_df['column'] = tar_cols
col_df['train_1_column'] = tr_first_cols
col_df['train_2_column'] = tr_second_cols

col_df

	column	train_1_column	train_2_column
0	elastase_inhibitor	g-99	g-5
1	bacterial_cell_wall_synthesis_inhibitor	g-194	g-322
2	histone_lysine_methyltransferase_inhibitor	g-658	g-661
3	steroid	g-18	g-698
4	pkc_inhibitor	g-570	g-763

Let's select some random target columns and see how they deal with pairs of the highly correlated features.

def plot_scatter(col_df, index):
    analysis = pd.DataFrame()
    analysis['color'] = train_target[col_df.iloc[index]['column']]
    analysis['x'] = train_features[col_df.iloc[index]['train_1_column']]
    analysis['y'] = train_features[col_df.iloc[index]['train_2_column']]
    analysis.columns = [
        'color', 
        col_df.iloc[index]['train_1_column'], 
        col_df.iloc[index]['train_2_column']
    ]
    analysis['size'] = 1
    analysis.loc[analysis['color'] == 1, 'size'] = 12

    fig = px.scatter(
        analysis, 
        x=col_df.iloc[index]['train_1_column'], 
        y=col_df.iloc[index]['train_2_column'], 
        color="color", 
        size='size',
        title='Scatter plot for ' + col_df.iloc[index]['column']
    )
    fig.show()
plot_scatter(col_df, 0)

We can extract several group names from target column names. Looks like that last term in column name is definition of a group. Let's extact them and visualize groups with number of columns > 1.

last_term = dict()

for item in target_columns:
    try:
        last_term[item.split('_')[-1]] += 1
    except:
        last_term[item.split('_')[-1]] = 1

last_term = pd.DataFrame(last_term.items(), columns=['group', 'count'])
last_term = last_term.sort_values('count')
last_term = last_term[last_term['count']>1]
last_term['count'] = last_term['count'] * 100 / 206

fig = px.bar(
    last_term, 
    x='count', 
    y="group", 
    orientation='h', 
    title='Groups in target columns (Percent from all target columns)', 
    height=500
)
fig.show()

Conclusion of basic EDA:

• At least 50 target columns have number pf positive samples less than 20 (about 0.1%)
• The biggest number of positive samples for 1 target column is 3.5%. So we deal here with highly imbalanced data.
• Cell variation features are highly correlation among themselvesand can be used to create new features.
• Data is quite skewed , which means some statistical features can be also be created.
• Drug_id can play an important role for cross-validaton strategy as explained by this topic. The Cross Validation strategy i will use is Multi-label stratified Kfold.
• All Targets are highly correlative can be used for transfer learning.

Feature engineering:

GENES = [g for g in train_features.columns if g.startswith("g-")]
print(f"Number of gene features: {len(GENES)}")
CELLS = [c for c in train_features.columns if c.startswith("c-")]
print(f"Number of cell features: {len(CELLS)}")

Number of gene features: 772
Number of cell features: 100

Now I check distributions of g- and c- of train and test set to see whether they are spicky or normally distributed.

train set before using RankGauss

gnum = train_features[GENES].shape[1]
graphs = []

for i in range(0, gnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=771:
            break
        graph = sns.distplot(train_features[GENES].values[:,item], ax=axs[k])
        graph.set_title(f"g-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

cnum = train_features[CELLS].shape[1]
graphs = []

for i in range(0, cnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=100:
            break
        graph = sns.distplot(train_features[CELLS].values[:,item], ax=axs[k])
        graph.set_title(f"c-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

test set before using RankGauss

gnum = test_features[GENES].shape[1]
graphs = []

for i in range(0, gnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=771:
            break
        graph = sns.distplot(test_features[GENES].values[:,item], ax=axs[k])
        graph.set_title(f"g-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

cnum = test_features[CELLS].shape[1]
graphs = []

for i in range(0, cnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=100:
            break
        graph = sns.distplot(test_features[CELLS].values[:,item], ax=axs[k])
        graph.set_title(f"c-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

From the plot I found that they are spiky distribution rather than normal distribution. Regardless of the train and test, they look be in the same shape.

It appears that the gene expression data and cell viability data can be controlled by the experimenter, so it is safe to assume that these data are independent of each other.

Also, since the shape of the distribution is close to normal distribution to begin with, I don't think there is much of a problem if it is forced to be transformed into a Gaussian distribution. Therefore I add RankGauss here and confirm the shape again here.

#RankGauss
for col in (GENES + CELLS):

    transformer = QuantileTransformer(n_quantiles=100, random_state=0, output_distribution="normal")
    vec_len = len(train_features[col].values)
    vec_len_test = len(test_features[col].values)
    raw_vec = train_features[col].values.reshape(vec_len, 1)
    transformer.fit(raw_vec)

    train_features[col] = transformer.transform(raw_vec).reshape(1, vec_len)[0]
    test_features[col] = transformer.transform(test_features[col].values.reshape(vec_len_test, 1)).reshape(1, vec_len_test)[0]

gnum = train_features[GENES].shape[1]
graphs = []

for i in range(0, gnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=771:
            break
        graph = sns.distplot(train_features[GENES].values[:,item], ax=axs[k])
        graph.set_title(f"g-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

cnum = train_features[CELLS].shape[1]
graphs = []

for i in range(0, cnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=100:
            break
        graph = sns.distplot(train_features[CELLS].values[:,item], ax=axs[k])
        graph.set_title(f"c-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

gnum = test_features[GENES].shape[1]
graphs = []

for i in range(0, gnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=771:
            break
        graph = sns.distplot(test_features[GENES].values[:,item], ax=axs[k])
        graph.set_title(f"g-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

cnum = test_features[CELLS].shape[1]
graphs = []

for i in range(0, cnum -1 , 7):
    #for least display.... 
    if i >= 3:
        break
    idxs = list(np.array([0, 1, 2, 3, 4, 5, 6]) + i)
    

    fig, axs = plt.subplots(1, 7, sharey=True)
    for k, item in enumerate(idxs):
        if item >=100:
            break
        graph = sns.distplot(test_features[CELLS].values[:,item], ax=axs[k])
        graph.set_title(f"c-{item}")
        graphs.append(graph)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

It appears that I have transformed the distribution of each data to resemble a normal distribution, as intended.

Dimensionality Reduction (PCA)

PCA is a linear transformation that projects the data into another space, where vectors of projections are defined by variance of the data. PCA results can be evaluated with reconstruction error and cumulative percent variance.

Since it's a high dimensional feature space, first we choose the optimal number of PCA.

for col in (GENES + CELLS):
    transformer = QuantileTransformer(random_state=0, output_distribution="normal")
    vec_len = len(train_features[col].values)
    vec_len_test = len(test_features[col].values)
    raw_vec = train_features[col].values.reshape(vec_len, 1)
    transformer.fit(raw_vec)

    train_features[col] = transformer.transform(raw_vec).reshape(1, vec_len)[0]
    test_features[col] = transformer.transform(test_features[col].values.reshape(vec_len_test, 1)).reshape(1, vec_len_test)[0]
    
data = pd.concat([pd.DataFrame(train_features[GENES]), pd.DataFrame(test_features[GENES])])

pca = PCA().fit(data)

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12,6)

fig, ax = plt.subplots()
xi = np.arange(1, 773, step=1)
y = np.cumsum(pca.explained_variance_ratio_)

plt.ylim(0.0,1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')

plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 750, step=50)) #change from 0-based array index to 1-based human-readable label
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')

plt.axhline(y=0.95, color='r', linestyle='-')
plt.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=16)

ax.grid(axis='x')
plt.show()

In this case, to get 95% of variance explained I need 600 principal components.

Next stage: improve CV score of the NN model built later

With number of pc decided, I am thinking about how to make use of the PCA set and did clustering to improve the CV score and Lb score of my NN model used to make multiclass prediction. I got inspired from the notebook posted on kaggle https://www.kaggle.com/namanj27/new-baseline-pytorch-moa?scriptVersionId=42917782. It provides the following ideas:

• Added features from PCA to existing ones instead of only use exisiting features to improve cv scores
• Added feature selection feature Selection using Variance Encoding with VarianceThreshold method of sklearn to improve cv scores
• Getting model updated with increased seeds

In the next report, I follow the idea of this notebook, and integrate it with the scaling process that I did just now. I added the idea of RankGauss to g- and c- columns for preprocessing, which processed the data to normal distribute-like data, and see it's effect to scores.

I also made the following changes based on this notebook after several trials (which are the strategies that I found most useful) and trained three models repectively to compare the results:

• Doing feature Selection over GENES/CELLS columns respectively for each model

• Train model with multiple seeds and average the predictions

• Tune hyperparameter for each model with different folds

• Combine 1-5 layers deep neural network and get predictors from public kernels for the resnet type model

Detail codes and result are in the second coding notebook.

REFERENCES

[1] https://clue.io/repurposing

[2] https://depmap.org/portal/download/Lamb

[3] https://lincsproject.org/

[4] https://www.kaggle.com/namanj27/new-baseline-pytorch-moa?scriptVersionId=42917782

[5] http://fastml.com/preparing-continuous-features-for-neural-networks-with-rankgauss/

[6] https://scrnaseq-course.cog.sanger.ac.uk/website/cleaning-the-expression-matrix.html

[7] J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet J-P, Subramanian A, Ross KN, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2005;313(5795):1929–35.

[8] Corsello, S.M., Nagari, R.T., Spangler, R.D. et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer 1, 235–248 (2020). https://doi.org/10.1038/s43018-019-0018-6

[9] Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., Lahr, D. L., Hirschman, J. E., Liu, Z., Donahue, M., Julian, B., Khan, M., Wadden, D., Smith, I. C., Lam, D., Liberzon, A., … Golub, T. R. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 171(6), 1437–1452.e17. https://doi.org/10.1016/j.cell.2017.10.049

[10] Townes, F.W., Hicks, S.C., Aryee, M.J. et al. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol 20, 295 (2019). https://doi.org/10.1186/s13059-019-1861-6